In a groundbreaking move to redefine the boundaries of neurotechnology and artificial intelligence (AI), Naxon Labs is proud to announce its integration with ZirconTech, a global leader in software development and emerging technologies. Effective January 2, 2025, Naxon Labs will operate under the ZirconTech banner, ushering in a new era of innovation and growth. This strategic transition combines Naxon Labs' pioneering expertise in neurotechnology with ZirconTech's advanced capabilities in software development, artificial intelligence and cloud computing. Together, they aim to create cutting-edge solutions that will contribute to the neurotechnology landscape and unlock new opportunities for customers, partners, and stakeholders worldwide. The merger promises significant advantages that will redefine the neurotechnology and AI landscape. Naxon Labs' flagship products, Naxon Explorer and Naxon Emotions, will leverage ZirconTech's robust infrastructure and advanced R&D capabilities, ensuring continued product support while fostering innovative advancements. Additionally, this integration expands the possibilities for applying AI to neurotechnology, including groundbreaking developments in brain imaging and EEG applications. Academic and industrial partnerships are also set to flourish, with access to enhanced resources and a renewed focus on scalability and impactful innovation driving collaborative growth. Current customers and users of Naxon Explorer and Naxon Emotions can rest assured that their products will remain operational and fully supported. This seamless transition underscores the commitment to maintaining trust and delivering value while enhancing offerings through ZirconTech's expertise. By joining ZirconTech, Naxon Labs positions itself at the forefront of artificial intelligence applied to neurotechnology innovation. This integration not only reinforces ZirconTech's leadership in emerging technologies but also broadens its portfolio with neurotechnology expertise. This integration marks the beginning of a collaborative journey that will see the development of advanced neurotechnology services, expanded research initiatives, and innovative applications of AI. Both companies invite customers, partners, and the broader technology community to explore these new opportunities and contribute to shaping the future.  

The convergence of quantum computing and neuroscience has opened an exciting new frontier, offering the potential to revolutionize how we analyze and interact with the human brain. Advancements in quantum algorithms and neurotechnology are making it increasingly feasible to process and interpret vast, intricate neural datasets. Naxon Labs has embarked on an exploratory research initiative to investigate the intersection of these fields, leveraging quantum computing to push the boundaries of neuroscience.     By Saiyam Sakhuja and Naxon Labs   Our research focuses on the practical application of quantum techniques, such as the Quantum Fourier Transform (QFT) and Digital-Analog Quantum Computing (DAQC), to enhance the analysis of brain-wave data. Collaborations with quantum platforms like PennyLane from Xanadu enriched this effort, enabling us to test novel algorithms and tools in real-world neurotechnological contexts.   Research highlights The research explored multiple quantum techniques and tools to tackle specific challenges in neuroscience: 1. Quantum Fourier Transform (QFT) The QFT was implemented to analyze the frequency components of EEG signals, offering a quantum alternative to classical Fourier Transforms. The results suggested: Advantages: Theoretical efficiency in processing large datasets and identifying frequency components. Challenges: Noise in real-world EEG data and limitations in current quantum hardware highlighted the need for further optimization of quantum circuits. 2. Quantum Wavelet Transform (QWT) The QWT was investigated for non-stationary signal analysis. Unlike its classical counterpart, the QWT produces quantum states that, when measured, yield probabilities rather than deterministic coefficients. Key insights include: Current quantum hardware struggles to translate QWT results into interpretable components analogous to classical wavelet coefficients. Further research is required to bridge the gap between quantum outputs and their practical applications in neuroscience. 3. Digital-Analog Quantum Computing (DAQC) A hybrid approach combining digital and analog quantum gates was used to optimize circuit depth for QFT tasks: The banged DAQC circuit introduced a discrete pulse control mechanism to minimize noise and computational overhead while improving efficiency. Algorithmic experiments suggested potential reductions in circuit depth but also pointed to challenges in maintaining hardware precision and achieving control accuracy. 4. Collaboration with Xanadu’s PennyLane Using PennyLane, we implemented workflows and explored tools for: Importing quantum workflows (pennylane.from_qasm) to integrate QASM-based circuits. Circuit inspection and resource optimization through qml.resource, which provided insights into gate counts and qubit usage. Hybrid classical-quantum execution on simulators and real quantum devices to validate algorithms. The integration with PennyLane allowed resource-efficient experimentation and debugging of quantum circuits.   Insights from data encoding and preprocessing Preprocessing neural data for quantum systems is critical for achieving meaningful results. The research adopted amplitude encoding to transform EEG data into quantum-compatible formats. Key considerations included: Normalization: Data was normalized and encoded using the AmplitudeEmbedding function in PennyLane to ensure compatibility with quantum circuits. Preprocessing Steps: Filters were applied to reduce noise and isolate signal components relevant to the research focus. Future Directions: Expanding data encoding to support multiple EEG channels and exploring real-device testing to account for noise and error.   Challenges and opportunities While quantum computing holds transformative potential, its integration into neuroscience faces several hurdles: Hardware limitations: Limited qubit counts, coherence times, and gate fidelities in current quantum processors remain significant bottlenecks. Algorithm maturity: Quantum algorithms require further development to address domain-specific challenges in neuroscience. Interpreting quantum results: Translating quantum outputs, particularly from QWT, into actionable insights is an open area of research. These challenges offer opportunities for interdisciplinary collaboration and innovation, highlighting the importance of partnering with academic and industrial leaders in quantum computing.   Future directions Building on this research, Naxon Labs envisions the following paths for further exploration: Hybrid Approaches: Combining classical and quantum methods to maximize efficiency and practicality. Scalable Quantum Algorithms: Developing algorithms tailored to large-scale neural datasets. Collaborations: Continue developing capabilities and partnerships to refine stepwise Digital-Analog Quantum Computing (sDAQC) and banged Digital-Analog Quantum Computing (bDAQC) methodologies, which aim to optimize quantum circuit execution.   This initial research represents a step toward realizing the potential of quantum computing in neuroscience, offering insights into how emerging technologies can address longstanding challenges in the field. As quantum hardware and algorithms evolve, their role in transforming neurotechnology will become increasingly significant.   About the Author Saiyam Sakhuja is a Quantum Application Engineer at CDAC Noida, a premier R&D organization under the Ministry of Electronics and Information Technology, India. He holds a Master’s in Physics from the National Institute of Technology, Trichy, and has a strong passion for quantum computing and its applications. Saiyam's research spans Quantum Signal Processing, Quantum Machine Learning, and Digital-Analog Quantum Computing (DAQC). He has contributed to various industry and academic projects, including applying quantum technologies to neuroscience data. Saiyam combines his technical expertise in Python and quantum programming with a deep curiosity for exploring emerging technologies.   References Parra-Rodriguez, A., Lougovski, P., Lamata, L., Solano, E., and Sanz, M., 2020. Digital-Analog Quantum Computation. Physical Review A, 101(2), p.022305. Canelles, V.P., Algaba, M.G., Heimonen, H., Papić, M., Ponce, M., Rönkkö, J., Thapa, M.J., de Vega, I., and Auer, A., 2023. Benchmarking Digital-Analog Quantum Computation. arXiv preprint arXiv:2307.07335. Martin, A., Lamata, L., Solano, E., and Sanz, M., 2020. Digital-Analog Quantum Algorithm for the Quantum Fourier Transform. Physical Review Research, 2(1), p.013012.

Glioblastoma multiforme (GBM) stands as one of the most aggressive and challenging brain tumors to diagnose and treat, posing significant obstacles in neuro-oncology. This blog explores how advancements in artificial intelligence (AI) are revolutionizing glioblastoma detection, bridging the gap between traditional diagnostic methods and cutting-edge technology. Combining insights from neuroscience and AI, this collaborative effort between Naxon Labs and ZirconTech investigates the application of state-of-the-art machine learning models, such as ResNet and Vision Transformer, to MRI imaging for glioblastoma classification. Highlighting both the challenges and breakthroughs, this post delves into the technical methodologies, findings, and the transformative potential of AI in neurodiagnostics, setting the stage for future innovations in healthcare.   Authors: Michelle Silveira (Biomedical Science BSc (Hons), University of Sussex), Essam Sheikh (B.Sc. in Artificial Intelligence/Computer Science, University of Birmingham), and Naxon Labs   Introduction: tumors, brain health, and the glioblastoma challenge Brain tumors present a unique challenge in medicine due to their complexity and the critical nature of the organ they affect. Among the various types of brain tumors, glioblastoma multiforme (GBM) stands out as the most aggressive, accounting for approximately 60% of all brain tumors in adults. This grade 4 glioma is notorious for its rapid growth, ability to infiltrate healthy brain tissue, and poor prognosis. Detecting glioblastomas is inherently difficult due to their heterogeneity in size, shape, and texture, and the overlap in imaging characteristics with healthy brain tissue or other tumor types. Traditional diagnostic methods, including physical examination, MRI, CT scans, and biopsy, often lack the precision needed for early and accurate detection. This gap in precision creates a need for innovative solutions, such as artificial intelligence (AI), to enhance diagnostic accuracy and treatment planning.   Overview of brain tumour types, diagnosis and treatment Our brain, an essential organ, is classed as one of the most complex and crucial parts of the human body and can be associated with intelligence, interpretation of senses, as well as the commander of the body that provides instruction and controls the body functions. Based on its unique functions, the brain can be classified into three main units: the forebrain, the midbrain and the hindbrain. The hind brain comprises the brain stem, upper part of the spinal cord and cerebellum which is known to play a key role in the coordination of the movements within the body. The voluntary movements of the body are controlled by the midbrain, which sits on the uppermost part of the brainstem. The forebrain, the largest, the most highly developed part compared to the other two units consists of the cerebrum, an important feature of the human brain which can interpret sounds, sights and touches (National Institute of Neurological Disorders and Stroke, 2023). Tumour classification When the brain is healthy it functions quickly and automatically, however, any abnormal growth of mass cells in or around the brain can lead to and contribute towards brain tumours. Over 150 brain tumours have been identified by the researchers and are known to be either malignant or benign and its fatality would depend on its severity. Irrespective of its type, both malignant and benign tumours, can have a profound effect on brain functions, and health and may impact the surrounding nerves, blood vessels and tissues in case of growth. Tumours that develop and are contained in the brain are called primary tumours, while the secondary or metastatic tumours form in a different part of the body and gradually spread towards and eventually in the brain where they stick and form a new tumour. We will primarily focus on benign tumours formed in the brain, such as meningiomas, pituitary adenomas, and malignant tumours like gliomas (Cleveland Clinic, 2022). Detailed overview of specific tumour types The pituitary gland is an organ located at the base of the brain and behind the nose. Any unusual growth formed over time in the gland may give rise to pituitary tumours, also known as pituitary adenomas which affect the amount of hormones released. These hormones tend to control body functions like growth, blood pressure and reproduction within the body. Most of these pituitary tumours are benign and do not typically spread around the body. There are four different classes of adenomas known, namely, functioning, nonfunctioning, macroadenomas and microadenomas. Functioning adenomas make hormones which cause other symptoms which solely depend on the type of hormone that it secretes. The symptoms range from tiredness or weakness, changes in the menstrual cycle, acne, thicker and more visible body hair, joint pain, fertility issues enlarged testicles, and higher levels of testosterone to name a few. One of the risk factors involved is the genetic predisposition of this tumour even though most people with this type of tumour do not have a family history of having it (Mayo Clinic, 2019). Meninges are membranes that provide protection and support to the brain and the spinal cord. Uncontrolled growth occurring in these meninges are called meningiomas which are the most common type of brain tumours and grow slowly over the years. The tumour may press on the nearby brains, nerves and vessels and can lead to serious disability. The symptoms may begin gradually over a period and include changes in vision, headaches, hearing loss, memory loss, and seizures among other symptoms. The cause for this type oftumour is unknown, however being exposed to radiation, female hormones (since they are prevalent in women), an inherited nervous system condition and obesity can greatly contribute as risk factors (Mayo Clinic, 2024). Shifting the focus to malignant tumours, glioma, a primary brain tumour forms when glial cells grow out of control. These glial cells form a backbone for the working of the central nervous system and support nerves. These gliomas include astrocytomas (including glioblastomas), ependymomas and oligodendrogliomas. Symptoms may include dizziness, headaches, cognitive problems, nausea and vomiting, seizures aphasia, difficulty maintaining balance and others. Age, ethnicity, family history, gender and radiation are risk factors that increase the likelihood of getting this tumour. Research suggests that the main cause of gliomas is the mutations in the DNA which lead to uncontrolled growth of cells. (Cleveland Clinic, 2021)   BRAIN TUMOURS Any uncontrolled growth of mass cells in and around the body can contribute towards brain tumours. There are over 150 brain tumours identifies by the researchers and these can be classified into two main classes 1) Benign: Non-cancerous, examples include pituitary adenomas, meningiomas, gangliocytomas ,Schwannomas 2) Malignant: Cancerous, example includes gliomas which can be divided into four main types ie; astrocytoma, ependymomas, glioblastoma, oligodendroglioma 3) glial tumours – composed of glial cells 4) Non- glial tumours : formed on or in the structure of the brain   WHAT ARE GLIOMAS? • Gliomas are tumours that develop when glial cells grow out of control. • Glial cells contribute by supporting the central nervous system . • These are known to be primary tumours since they originate and grow in the brain. • These can be extremely life threatening since they can be hard to reach and treat with surgery and eventually grow into other areas in the brain. • One particular glioma of interest : GLIOBLASTOMA   GLIOBLASTOMA(GBM) • GBM , also refereed to as Grade 1V astrocytoma is a fast growing and agressive brain tumour • This type of tumour is known to contribute for almost half of all cancerous brain tumours in adults. • GBM form in glial cells called astrocytes and are the fastest growing astrocytomas. • Certain risk factors like exposure to chemicals, genetic predisposition and any previous contact with radiation therapy to head can all contribute and greatly increase the risk of GBM.   Prevalence and incidence: GBM account for 47.7% of all malignant and CNS tumours. More prevalent in men compared to women and the median age of diagnosis is 64 years. The survival rate is quite low with approximately 40% survival in the first-year post diagnosis and 17% in the second year.   The cause of the malignant tumour is yet unknown. The symptoms include blurred vision, loss of appetite, persistent headaches, memory problems, mood or personality changes, muscle weakness, nausea and vomiting, seizures and speech problems.   Diagnostic methods for brain tumours Diagnosis for brain tumours entails an extensive number of tests and procedures to ensure a detailed and definite cause if there is one. These usually include a neurological exam wherein different compartments of the brain are examined. This exam includes checking vision, hearing, balance, coordination, strength and reflexes. Any impairment found in this exam does not suggest a brain tumour and therefore further analysis needs to be undertaken. A head CT scan, brain MRI, and PET scan are examples of procedures which help in the detection of any tumours within the brain. They work on different principles but serve the same purpose. Brain biopsy is another procedure which helps in diagnosis; however, it might be invasive in some cases since it involves the removal of a sample of the brain tumour tissue which is then further tested in the labs. The testing in labs can further help in identifying the severity of the brain tumour by grading them from 1 to 4 based on the growth of the tumour cells. Grade 1 and grade 2 are classified as the low-grade tumours, according to WHO while grade 3 and grade 4 is in a high-grade tumour due to their fastgrowing and aggressive behaviour. Furthermore, prognosis tells you how quickly the tumour can be cured based on prior diagnosis conducted. Various factors like the type of brain tumour, the growth, the location within the brain, the mutations in DNA, whether the tumour can be eradicated via surgery and an individual’s health and well-being can all influence one’s well-being and threat to life. MRI is the most important imaging studying for GBM. If the tumour picks up the contrast (turns brighter), it is an indication for a highergrade astrocytoma. Unlike GBM, low-grade tumours do not show a great contrast enhancement.  To compare, other imaging sequences provide clues as to brain swelling and brain infiltration. How does an MRI work (simplified)? A combination of magnets and radio waves are used to to create detailed images of the soft body tissue. A single MRI can produce hundreds of images representing different areas of the brain. Images are composed of sequences and slices. Sequences (T1, T2, etc…) refer to the type of image along with its characteristics. Slices are individual images taken of a body part in multiple layers. A combination of sequences are used to diagnose GBM along with all their slices. How does a radiologist diagnose GBM? A radiologist examines different MRI sequences, slice by slice, looking for tumour characteristics. Different sequences highlight various aspects of the brain. The most important sequences include T1, T2 and FLAIR. They combine their findings with clinical data, patient history, and, in many cases, biopsy results to confirm the diagnosis and assess the tumour aggressiveness. A definitive diagnosis is usually given after biopsy   MRS is an imaging tool based on MRI and provides more information on the chemical composition of the tumour. It works on the principle that certain chemicals are abundant in the normal brain cells, while some are abundant in the presence of a tumour. It is a non-invasive tissue sampling technique compared to a standard biopsy. However, it is not as definitive and accurate, therefore cannot completely rely on it.   Treatment modalities and their side effects Treatment for a brain tumour can vary depending on if it's benign or malignant. These include an array of combinations of treatments which might involve surgery, radiation therapy, radiosurgery, chemotherapy and targeted therapy. Surgery is one of the most used treatments and is used when the surgeon tries to remove most of the brain tumour cells if not all as it mostly depends on the size and location of the tumour within the brain. There are a various surgical approaches used to remove the brain tumour, and this will depend on its size and location craniotomy and neuroendoscopy are examples of this type of treatment. This treatment has its side effects and complications that can cause infection, bleeding, blood clots and injury to the brain tissues. Other forms of treatment like radiation therapy and radiosurgery utilise an intense form of X-rays and other forms of light that can be used to destroy the cancer cells in malignant tumours as well as slow growing benign tumours and are performed over a few sessions. The three different types of radiation therapies that are used are external beam radiation therapy, stereotactic radiosurgery and proton therapy. Physical side effects include fatigue, headaches, memory loss, scalp irritation, hair loss, changes in skin on the scalp and hair loss. One of the most conventional forms of treatment is chemotherapy where the patient is prescribed strong medicines to kill tumour cells in the form of pills or injected intravenously and side effects can vary depending on the type and dosage of the drug. Similarly, targeted drug therapy attacks specific chemicals present in the tumour cells, eventually blocking the chemicals and killing the tumour cells. Unlike chemotherapy, targeted therapies do not attack normal healthy tissues and therefore tend to have fewer and milder side effects. (Mayo Clinic, 2019) Importance of early detection and AI in detection Early detection of malignant brain tumours needs to be our key focus, even though, there have been some groundbreaking strides in this field, in terms of diagnosis and treatment for benign tumours. The introduction of Artificial intelligence in neuro-oncology, as suggested by some researchers has shown a significant promise in diagnosis, prognosis and treatment planning by effectively detecting and classifying brain tumours from medical images, which needs to be further explored. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumour delineation and characterisation.   Despite many clinal trials and decades of research, incurable brain tumours with grim prognosis exist such a glioblastoma. However, adoptive methods of AI in brain tumour diagnosis and treatment management would require careful considerations of ethical, legal and social implications and addressing concerns related to healthcare disparities.     The complexity of glioblastoma detection As an overview of the challenges associated with detecting glioblastomas, there are several critical issues: Variability in imaging characteristics: Glioblastomas exhibit irregular shapes and unpredictable locations within the brain, making it difficult to distinguish them from surrounding tissues on MRI scans. Data scarcity and quality: The availability of labeled, high-quality datasets for glioblastoma detection is limited. Most public datasets are skewed toward generalized brain abnormalities rather than glioblastoma-specific imaging. Need for multimodal analysis: A single imaging modality may not capture the complete picture. Combining imaging data with clinical history and other biomarkers could improve diagnostic capabilities. Resource constraints: Manual analysis by radiologists is time-intensive and prone to subjective interpretation, underscoring the need for automated tools to assist in diagnosis. Example: different types of glioblastoma, they can have multiple different, shapes, locations and appearances making them hard to classify In order to leverage AI to address these challenges, we explored the potential of machine learning models in reducing variability and increasing the speed and accuracy of glioblastoma detection. AI models for glioblastoma detection Building on Michelle’s foundational work at Naxon Labs, we partnered with ZirconTech, where Essam Sheikh took on the task of designing and testing AI models to tackle glioblastoma classification. Using publicly available datasets, such as those from Kaggle and Hugging Face, Essam evaluated two state-of-the-art models: Vision Transformer (ViT) and ResNet. Detecting Glioblastomas from Brain MRI Images Using AI Models We focused on leveraging AI to detect GBMs from MRI images by utilising classification models available on Hugging Face, such as Vision Transformer (ViT) and ResNet. These were then fine-tuned on MRI datasets sourced from Kaggle and other medical repositories. The Challenge of Glioblastoma Detection Glioblastomas are difficult to detect and diagnose due to their heterogeneity in size, shape, and location within the brain. This variability means that AI models must be trained on diverse data to identify GBMs accurately. However, one of the most significant challenges we encountered during this project was the scarcity of publicly available datasets focused on glioblastomas. While gliomas are relatively common, large annotated datasets focusing solely on GBM are rare. Most of the available data includes a mix of various glioma stages or requires special access from medical institutions, which can be difficult to obtain. Model Performance: ResNet vs Vision Transformer We fine-tuned both ViT and ResNet models for classification. ViT, which typically requires large datasets, struggled due to the limited GBM-specific data. ResNet performed better, achieving higher classification accuracy despite the smaller dataset, making it a more practical choice for this project.   To improve performance, integrating clinical data and expanding the dataset to include more diverse patient populations and other brain tumour types could enhance the model's ability to differentiate between tumours. ResNet showed potential, but more data is crucial for advancing the model's role in GBM detection.   The process   1. Dataset preparation Image quality: MRI images were preprocessed to enhance brightness and reduce noise. Focus on tumor regions: Efforts were made to extract and analyze slices most relevant to tumor presence.   2. Model evaluation Vision Transformer (ViT): ViT is a cutting-edge deep learning architecture designed for image recognition tasks. It excels when provided with large and diverse datasets. However, its performance was limited due to the data scarcity typical in glioblastoma classification. ResNet: ResNet, a robust convolutional neural network (CNN), demonstrated superior performance compared to ViT. Its ability to generalize well on smaller datasets made it particularly suitable for this project.   3. Model performance metrics Accuracy: ResNet achieved higher accuracy in classifying glioblastomas than ViT, making it the preferred choice. Efficiency: ResNet required less computational power and trained effectively with the available data.   4. Challenges and technical details Overfitting: Both models initially showed a tendency to overfit due to the limited dataset size. Regularization techniques and data augmentation were employed to mitigate this issue. Feature Extraction: ResNet’s architecture allowed for effective feature extraction from MRI images, leveraging its residual connections to maintain gradient flow and improve learning efficiency. Transfer Learning: Pre-trained ResNet models were fine-tuned on glioblastoma-specific data, further enhancing performance.   Results and findings The collaborative project between Naxon Labs and ZirconTech yielded interesting results: ResNet’s superiority: ResNet consistently outperformed ViT, achieving high classification accuracy and demonstrating its suitability for glioblastoma detection. Potential for real-world application: The findings suggest that ResNet-based AI models could assist radiologists by flagging suspicious regions in MRI scans and providing supplementary diagnostic tools. Importance of Data Diversity: The project underscored the need for more diverse and annotated datasets to improve model generalization and reliability.   Advancing neurotechnology with AI The collaboration between Naxon Labs and ZirconTech showcases the transformative potential of AI in addressing complex medical challenges like glioblastoma detection. By combining Michelle Silveira’s expertise in neuroscience and data analysis with Essam Sheikh’s technical skills in machine learning, the project bridged the gap between theory and practical application. Next Steps: building a future in AI-driven neurodiagnostics Looking ahead, the focus will be on: Expanding datasets: Collaborating with medical institutions to collect more diverse and high-quality glioblastoma imaging data. Integrating multimodal analysis: Combining MRI data with clinical and genomic information to enhance diagnostic accuracy. Optimizing deployment: Developing user-friendly tools that integrate seamlessly into clinical workflows, empowering healthcare professionals with AI-driven insights.   Join us in revolutionizing neurodiagnostics Naxon Labs and ZirconTech are committed to shaping the future of neurodiagnostics through innovation in AI and neurotechnology. We invite researchers, clinicians, AI professionals, and industry leaders to collaborate with us on groundbreaking projects that aim to enhance patient care and outcomes. Whether you are interested in contributing to advancing glioblastoma detection, exploring AI-driven solutions for medical imaging, or developing transformative neurotechnological tools, we want to hear from you. Connect with us today to discuss potential partnerships and opportunities: Email: contact@naxonlabs.com | contact@zircon.tech Learn More: Naxon Labs | ZirconTech Together, we can push the boundaries of what’s possible and create a future where technology and healthcare converge for the betterment of society.         References : National Institute of Neurological Disorders and Stroke (2023). Brain Basics: Know Your Brain | National Institute of Neurological Disorders and Stroke. www.ninds.nih.gov. https://www.ninds.nih.gov/healthinformation/public-education/brain-basics/brain-basics-know-your-brain Cleveland Clinic (2022). Brain Cancer & Brain Tumor: Symptoms, Causes & Treatments. https://my.clevelandclinic.org/health/diseases/6149-brain-cancer-brain-tumor Mayo Clinic (2019). Pituitary tumors - Symptoms and causes. https://www.mayoclinic.org/diseases-conditions/pituitarytumors/symptoms-causes/syc-20350548 Mayo Clinic (2024). Meningioma - Symptoms and causes. https://www.mayoclinic.org/diseasesconditions/meningioma/symptoms-causes/syc-20355643 Cleveland Clinic. (2021). Glioma: What Is It, Causes, Symptoms, Treatment & Outlook. https://my.clevelandclinic.org/health/diseases/21969-glioma Mayo Clinic (2019). Brain Tumor - Diagnosis and Treatment https://www.mayoclinic.org/diseasesconditions/brain-tumor/diagnosis-treatment/drc-20350088.    

ADHD (Attention-Deficit/Hyperactivity Disorder) is a complex neurodevelopmental condition affecting millions of individuals worldwide. Traditional diagnostic methods often rely on subjective assessments, leaving room for variability in interpretation. Recent advancements in neuroscience, particularly in EEG (electroencephalography) technology, have opened new pathways for objective analysis. This article delves into the use of EEG to analyze brainwave activity—specifically theta and beta waves—offering a deeper understanding of ADHD's neurobiological underpinnings and presenting innovative approaches for improving diagnostic accuracy and treatment outcomes.   By Abbie Bull, University of Birmingham, in collaboration with Naxon Labs   Introduction: The Neural Marker of ADHD – Theta/Beta Ratio Attention-Deficit/Hyperactivity Disorder (ADHD) is a complex neurodevelopmental condition characterized by inattention, hyperactivity, and impulsivity. One promising avenue for understanding and diagnosing ADHD lies in electroencephalography (EEG), which provides a window into brain activity patterns. Specifically, the theta/beta ratio (TBR) has emerged as a neural marker of ADHD. Theta waves (4–8 Hz) are associated with drowsiness and inattention, while beta waves (13–30 Hz) correlate with active thinking and focus. Individuals with ADHD often exhibit an elevated TBR, where theta waves outnumber beta waves, reflecting a reduced capacity for sustained attention. Clarke et al., 2011 Methodology: A Protocol to Analyze EEG Data The primary goal of this study was to design and evaluate a protocol to detect ADHD-related patterns using EEG data collected with Naxon Labs' Explorer tool and Muse headbands. The protocol included: Baseline and Task Recording: Participants completed a baseline resting phase followed by a Go/No-Go task, a cognitive test commonly used to assess attention and impulse control. The Go/No-Go task is often used in psychology to measure an individual's cognitive abilities such as impulse control, attention, and reaction time. In this task the participant must refrain from performing an action when a visual stimulus tells them not to do, for example avoid pressing the space bar when the screen is red. Whilst wearing the Naxon Muse EEG headband, the participant completes a 5-minute period of baseline resting activity before moving on to complete a series of Go/No-Go trials. We analyse the wave frequency in Naxon Explorer to see whether there appears to be a drastic difference in ratio between the theta and beta activity at baseline and during the trial. The EEG data is filtered to identify artefacts such as blinks and clenches which may disrupt the electrical activity recorded.  Data Analysis: EEG signals were processed to extract theta and beta wave activity using Naxon Explorer. The theta/beta ratio was calculated for specific electrodes (AF7, AF8, TP9, TP10) using statistical tools like Excel and SPSS. Visual and statistical comparisons were made between baseline and task-related activity. Thresholds for Interpretation: A TBR above 2 suggests ADHD-related patterns. Ratios between 1.2 and 2 indicate typical brain activity. A TBR below 1.2 reflects balanced neural activity, not consistent with ADHD. Findings: Distinct Neural Patterns Predictions - - If there are symptoms of ADHD, the brain activity should be dominated by theta waves. This should be more pronounced in the Go/No-Go task than in the baseline.  - If the brain activity is typical, the beta waves should be more pronounced to show the brain is engaging in attentional control.   Findings -  - For regions AF7 and AF8 it appears the activity is not dominated by theta waves, more so by beta waves. Whereas for regions TP9 and TP10, the theta activity is much greater than beta activity.    Visual Analysis: In the temporal regions (TP9 and TP10), theta activity was significantly higher than beta activity, indicative of ADHD-related patterns. Frontal regions (AF7 and AF8), however, displayed more balanced or beta-dominant activity, suggesting less ADHD-related activity. Statistical Analysis: Temporal electrodes (TP9 and TP10) showed TBR values as high as 7.81, far exceeding the typical range, supporting the hypothesis of ADHD-related activity. Frontal electrodes (AF7 and AF8) had lower ratios (e.g., 0.55, 0.86), which may reflect typical attentional processing in these areas. Limitations and Future Directions   Challenges with EEG-Based Diagnosis: Variability: The TBR may fluctuate with age, development, and individual differences. Overlap with Other Conditions: Similar brainwave patterns can appear in anxiety or other neuropsychiatric disorders. False Positives/Negatives: Variability in EEG data can lead to diagnostic inaccuracies.   Critiques of the theory: * Inconsistencies 🡪 not all studies have consistently found an elevated TBR in individuals with ADHD. The variability might be due to differences in study methodologies, participant characteristics, or EEG recording and analysis techniques. * Age and Development 🡪 the Theta/Beta ratio can change with age, and some of the differences observed might be related to developmental stages rather than ADHD. * Specificity and Sensitivity 🡪 there is ongoing debate about the specificity and sensitivity of TBR as a diagnostic tool. While it can indicate differences in brainwave activity, it might not be sufficient on its own for a definitive ADHD diagnosis without considering clinical assessments and other diagnostic criteria.   Critiques of using EEG: * Overlap with Other Conditions 🡪 brainwave patterns observed in ADHD can also be seen in other conditions such as anxiety. This lack of specificity means that an EEG-based diagnosis might lead to misdiagnosis if not corroborated by other clinical assessments * False Positives/Negatives 🡪 EEG-based methods may produce false positives (diagnosing ADHD when it’s not present) or false negatives (failing to diagnose ADHD when it is present). This could be due to the inherent variability in EEG data or the influence of external factors like fatigue, stress, or medication.  * Symptom Variability 🡪 ADHD is a highly heterogeneous disorder, meaning it manifests differently in different individuals. EEG patterns that may correlate with ADHD in one person might not apply to another, making it difficult to develop a universal EEG-based diagnostic criterion.     Enhancing Accuracy with AI: Integrating artificial intelligence (AI) with EEG analysis offers a pathway to address these challenges. AI can detect subtle trends in noisy data, adapt models as new data becomes available, and provide personalized insights for both diagnosis and treatment. This approach could revolutionize ADHD management by offering tailored interventions such as neurofeedback. There is evidently complications with the traditional approach to diagnosing ADHD which EEG can counteract. However, more still can be done to ensure diagnosis is robust and accurate across universal populations… Using AI, biomarkers of ADHD can be detected from noisy EEG data and transformed into meaningful trends and patterns often missed by the human eye.   As more EEG data is collected from patients, AI models can be continuously refined. Machine learning techniques can be used to update the models as new data comes in, ensuring that the diagnostic tool remains up-to-date with the latest understanding of ADHD. Clinicians and researchers can provide feedback on the AI’s performance, leading to iterative improvements in the model’s accuracy and reliability. However extremely large sample sizes are needed to establish a tool which is not confounded by demographic characteristics such as co-morbid conditions.  Diagnosis is not the only focus! Combining AI with EEG can lead to the development of personalised treatment plans for patients with ADHD. Based on their individual patterns of brain activity, interventions such as neurofeedback can be tailored to help patients manage their symptoms.   Ethical Considerations As technology advances, integrating tools like EEG and AI into ADHD diagnosis and treatment raises important ethical questions. One concern is the increasing reliance on technology in clinical practice. Some clinicians argue that while these tools provide valuable insights, they should not replace the human element of diagnosis and treatment, which includes understanding the patient's lived experience and context. Looking ahead, the potential for EEG and AI to autonomously diagnose and implement treatment plans sparks debate. While such innovations could streamline healthcare delivery, they also raise questions about the role of clinicians and the ethical implications of relying on machines to make critical health decisions. Another contentious issue is the ability to "read minds" through brain imaging techniques like EEG. This raises concerns about privacy and consent, as participants may be wary of how their neural data could be interpreted or used beyond the intended scope of diagnosis. Lastly, the uneven accessibility to advanced diagnostic tools poses significant challenges. Socioeconomic disparities may limit access to EEG and AI-based solutions, leading to unequal opportunities for accurate diagnosis and effective treatment. Addressing these ethical concerns is crucial to ensure that technological advancements in ADHD care are implemented responsibly and equitably. . A Step Toward More Accurate ADHD Diagnostics This project underscores the potential of EEG technology, particularly the theta/beta ratio, as a tool for understanding ADHD. While there are limitations, the integration of advanced analytics, such as AI, could enhance the reliability and applicability of these findings. The work conducted during this project represents an important foundation for further research and development in ADHD diagnostics and personalized treatment. For researchers, clinicians, and technologists, this collaboration between Naxon Labs and the University of Birmingham highlights the power of interdisciplinary innovation in addressing complex neurological conditions. About the Author Abbie Bull is a Psychology student at the University of Birmingham. During her internship with Naxon Labs, she explored the potential of EEG-based diagnostics for ADHD, contributing to groundbreaking research in neurotechnology.

The intersection of neurotechnology and virtual reality (VR) holds immense potential for revolutionizing various fields, including education, healthcare, therapy, and user experience. This post delves into how VR, combined with neurotechnology, can enhance learning outcomes, improve therapeutic interventions, and offer innovative solutions for user interaction.   By Jay Torres-Carrizales Psychology at Arizona State University (USA)   Education: Enhancing Learning with Wearable Neurotechnology   Real-Time Human-in-the-Loop Education Systems The transformative potential of wearable neurotechnology in educational settings heralds the "Future of Smart Classrooms in the Era of Wearable Neurotechnology." Envision a real-time human-in-the-loop education system, where the portability, comfort, and wireless data transfer capabilities of devices like EEG headbands enable continuous monitoring and analysis of class engagement and social dynamics. This innovative approach allows educators to gain deeper insights into cognitive processing, understand how students interact and process information, and predict learning patterns. By leveraging this data, teachers can tailor their strategies to enhance individual student outcomes, creating a more adaptive, personalized, and effective learning environment.   Case Study: Babini et al. A study by Babini et al. demonstrated the effectiveness of using wearable EEG devices in a virtual reality (VR) environment to enhance student focus and learning. The results showed higher engagement and better performance in VR settings, suggesting that such technologies can significantly improve educational experiences. This study highlights the potential of combining EEG and VR to create immersive learning environments that cater to the individual needs of students, providing them with personalized feedback and support. Physiological state and learning ability in normal and VR conditions: By recording EEG and facial EMG signals of participants during stimulation, the research demonstrated that both the brain and facial muscles exhibited greater fractal dimensions in the 3D video condition, indicating a more substantial reaction compared to 2D videos. This heightened physiological response was paralleled by improved learning outcomes, as students correctly answered more questions in the VR environment. These results suggest that VR not only enhances engagement but also improves the effectiveness of learning by stimulating more profound neural and muscular responses.   Applications Virti: Training and learning for surgeons, engineers, and nurses. Virti uses VR and AR to create realistic simulations that help professionals develop critical skills in a safe and controlled environment. By integrating EEG data, Virti can further enhance these training programs by providing real-time feedback on cognitive load and stress levels. Hololens: Augmented reality for immersive educational experiences. Hololens can be used to create interactive and engaging lessons that adapt to the cognitive state of the user, ensuring that they remain focused and engaged throughout the learning process.   Healthcare: Innovative Therapies and Rehabilitation   XR Health: Physical and Occupational Therapy Extended reality (XR) technologies combined with EEG can facilitate patient motivation and participation in rehabilitation. These tools are particularly beneficial for individuals recovering from strokes or other conditions that impair mobility and cognitive function. By providing real-time feedback on brain activity, therapists can tailor rehabilitation programs to the specific needs of each patient, ensuring optimal outcomes.   McGill University and Shriner Hospital Using VR to reduce chronic pain and monitor emotions and stress during medical procedures. EEG data can provide valuable insights into patient responses, enhancing the effectiveness of these interventions. For example, by monitoring brain activity during pain management sessions, clinicians can identify the most effective strategies for reducing discomfort and improving patient well-being.   Therapy: Addressing PTSD, Phobias, and Addictions   PTSD and Phobia Treatment VR environments can recreate trauma scenarios, allowing patients to confront and manage their emotions safely. EEG monitoring can track stress levels and improvements, helping therapists tailor treatments more effectively. This approach can be particularly beneficial for individuals with PTSD, as it allows them to process traumatic memories in a controlled and supportive environment.   Addiction Prevention: Vaping Growing evidence suggests that repetitive transcranial magnetic stimulation (rTMS), a non-invasive form of electromagnetic brain stimulation used to modulate neural activity, may be useful in the treatment of addiction. In a current study, participants engaged in tasks that involved using a virtual hand that mirrored their actual hand movements to either search for and destroy vapes or search for and throw tennis balls. This approach aims to explore how immersive VR environments combined with rTMS can influence behavior and neural pathways related to addiction, offering a potential new avenue for treatment and prevention.   Technology: Enhancing User Experiences and Comfort   Autopilot Vehicle Testing Studies on how comfortable people feel in autopilot vehicles can benefit from EEG monitoring to identify and address discomfort factors. VR simulations combined with EEG can provide comprehensive data on user experiences, helping designers create more comfortable and user-friendly vehicles. By understanding the neural responses to various driving scenarios, manufacturers can improve the safety and comfort of autonomous vehicles.   VTuber and VR Chat Using EEG headbands to control avatar expressions and movements in virtual environments. This technology can offer more natural and responsive interactions for VTuber users and VR chat participants. By monitoring brain activity, the system can adjust the avatar's expressions and movements in real-time, creating a more immersive and engaging experience.   The integration of neurotechnology with VR is poised to bring significant advancements in various sectors. From enhancing learning experiences to improving therapeutic interventions and user interactions, the possibilities are vast and promising. By leveraging the power of EEG and VR, we can create more personalized, effective, and engaging solutions that address the unique needs of each individual.     Stay tuned for more updates and insights as we continue to push the boundaries of neurotechnology and AI.

February 27, 2024, TheStudio Birmingham, 7 Cannon Street, Birmingham, B2 5EP The neurotechnology landscape is rapidly evolving, and Innovate UK's Neurotechnology Conference held in Birmingham was a testament to the vibrant innovation and collaborative spirit permeating this field. The conference not only showcased cutting-edge advancements but also provided a platform for experts to forge connections and explore potential partnerships.   By Olivia Fox BSci Biological Sciences at University of Birmingham   The conference was designed as a collaborative space for attendees to explore the latest neurotechnology innovations, engage with pioneers in the field, and forge partnerships that could drive the next wave of breakthroughs in this exciting field. The day was packed with insightful presentations and networking opportunities, highlighting the latest advancements and discussing the integration of these technologies into healthcare and beyond. Event Highlights and Insights The day was packed with presentations from leaders in the industry and academia, each providing unique insights into their work and the broader implications for health and technology. From biomagnetic sensing to neural stimulation and quantum sensors for brain imaging, the conference covered a broad spectrum of neurotechnology applications.   Highlighting the Experts   Dr. Emil Hewage – Pioneering AI in Neurotechnology Dr. Emil Hewage, the CEO & Founder of BIOS Health, delivered a compelling presentation on the integration of artificial intelligence with neurotechnology. His focus on 'AI as the key to the neural code' provided deep insights into how cutting-edge AI techniques are being used to decode and interact with complex neural signals. BIOS Health is leading the charge towards creating more intuitive and powerful neural interfaces. Jane Ollis – Stress Management through Neurodigital Tools Jane Ollis, the CEO & Founder of Mindspire, discussed the role of neurodigital technologies in managing stress and enhancing mental health. Her talk showcased how Mindspire’s innovative solutions are using neurotechnology to provide real-time stress management tools, illustrating the potential for these technologies to improve everyday health.  Prof. Kia Nazarpour – Innovations in Neural Prosthetics As the Chief Strategy Officer of Neuranics, Prof. Kia Nazarpour presented on the advancements in neural prosthetics and their integration into digital health systems. His insights into the development of devices that enhance or restore human capabilities highlighted the transformative potential of neural prosthetics.  Prof. Keith Mathieson – Advancements in Neurophotonics Professor Keith Mathieson, from the University of Strathclyde, provided an overview of the emerging field of neurophotonics. His role as the Royal Academy of Engineering Chair in Emerging Technologies has positioned him at the forefront of developing technologies that use light to map and understand brain functions.  Dr. Luke Bashford & Dr. Anna Kowalczyk – Cutting-Edge Research in Neurotechnology Dr. Luke Bashford and Dr. Anna Kowalczyk, both esteemed academics in the field of neuroscience and neurotechnology, discussed their current research projects. Dr. Bashford, from the University of Newcastle, and Dr. Kowalczyk, from the University of Birmingham, covered a variety of topics including neural stimulation and the psychological aspects of neurotechnology applications. Rahman Shama, CEO of NeuroCreate, shared her compelling insights on achieving 'Flow' states through neurotechnology. NeuroCreate is at the forefront of developing AI-powered tools designed to enhance creativity and cognitive agility. Their platform leverages the neuroscientific principles underlying creative thinking, personalized by AI to augment and streamline work processes. By fostering 'Flow' states, NeuroCreate not only boosts productivity and efficiency but also enhances mood and wellbeing, effectively reducing stress. Rahman's discussion emphasized the transformative potential of wearable technologies in accessing these peak performance mental states, making a strong case for collaborative ventures with companies like Naxon to further explore relaxation and stress management applications. Dr Marcus Kaiser leads the Dynamic Connectome Lab - a research group dedicated to understanding the complex organization and dynamics of brain networks. Dr. Kaiser and his team disseminate their research findings, tools, and resources to the broader scientific community. The Dynamic Connectome Lab focuses on developing innovative methods for analyzing and modeling brain connectivity data, with a particular emphasis on dynamic changes in neural networks over time. "Changing Connectomes" explores the dynamic nature of brain networks and their implications for understanding brain function and dysfunction. His research sheds light on how changes in brain connectivity contribute to various neurological and psychiatric disorders, offering new insights into potential therapeutic interventions.  Other participants included Dr Charlie Appleby-Mallinder (Sector Engagement Manager – Medical & Healthcare, Advanced Manufacturing Research Centre), Dr Jacques Carolan (Programme Director, Advanced Research + Innovation Agency (ARIA)), Neurotech Networks, CloseNIT, CPNN+, N-CODE, Neuromod+ and Respect4Neurodevelopment.   One of the most exciting aspects of the conference was the enthusiasm for collaboration, shared by participants during networking breaks. Many attendees, including Naxon Labs' Olivia Fox, engaged in meaningful discussions that sparked ideas for potential research collaborations and technology development. Olivia, representing Naxon Labs at the event, highlighted the company's commitment to advancing neurotechnology through its innovative platforms like Naxon Explorer. Engaging with the ideas presented, she identified several opportunities where Naxon Labs could apply its technologies to the projects and initiatives discussed at the conference. Innovate UK's Neurotechnology Conference was not just a showcase of technological advancements but a beacon for future collaborations that could shape the landscape of neuroscience and healthcare. As we reflect on the knowledge shared and connections made, it's clear that the path forward is one of collaborative innovation. Naxon Labs remains at the forefront, ready to contribute to and benefit from these exciting developments. The journey of exploring the mind and enhancing human capabilities continues, with each discovery and partnership bringing us closer to understanding the complex tapestry of the human brain.

Electroencephalography (EEG) technology offers a window into the brain's intricate electrical activities, revealing insights into our mental states, emotions, and cognitive processes. Naxon of Labs has been working with this technology through the Muse headband, a portable EEG device, to gather valuable neuronal information. We will explore the details and the rationale behind the strategic placement of sensors on the Muse headband, which is instrumental in the functionality of our Naxon Explorer and Naxon Emotions platforms. We will provide a basic introduction to system 10-20 and system 10-10, as with the new approach of Naxon Labs we will be able to work in software for any EEG system. Finally, we provide a summary of the placement of sensors in the Neuphony device, another partner with whom we are working with.   By Olivia Fox BSci Biological Sciences at University of Birmingham   International 10–20 system The 10-20 System is named for the method of determining electrode locations based on percentages of the distance across the skull. This ensures that electrodes are systematically placed at either 10% or 20% intervals of the total front-back or right-left distance of the head. This methodical approach allows for the detailed mapping of the brain's electrical activity during different states such as sleep and wakefulness. These measurements begin at key anatomical landmarks like the nasion (the area just above the bridge of the nose) and the inion (the highest point of the skull at the back). Electrodes in the 10-20 System are labeled according to the brain region they cover, with letters representing different areas (Fp for pre-frontal, F for frontal, T for temporal, P for parietal, O for occipital, and C for central). Additional labels include "Z" for midline electrodes, odd numbers for electrodes on the left side of the head, even numbers for those on the right, and "A" or "M" for those placed near the mastoid process behind the ear. The positioning and labeling of electrodes are critical for interpreting EEG data accurately. For instance, the TP9 and TP10 sensors on the Muse headband correspond to the temporal regions, crucial for emotional processing, while AF7 and AF8 cover the prefrontal cortex, important for emotion regulation. This alignment with the 10-20 System ensures that data collected from the Muse headband can be integrated and compared with broader EEG research and applications. Understanding the 10-20 System's intricacies, including measurements from nasion to inion and preauricular points, and the specific placement of electrodes around the skull, enhances our ability to capture and analyze brain activity reliably. This knowledge base supports the effective use of EEG technology in both clinical and research settings, providing a foundation for advancements in neuroscience and neurotechnology. Figure 1: International 10–20 system The Muse headband is equipped with four primary sensors, thoughtfully positioned to optimize the monitoring of various brain activities. These sensors detect the electrical signals generated by thoughts, emotions, actions, and reactions, enabling the analysis of brainwave patterns that correspond to different mental states such as relaxation, concentration, and emotional responses. Here's an in-depth look at the sensor positions and their importance: 1. TP9 and TP10: Temporal Lobes Location: Just behind the ears, covering the left and right temporal lobes. Importance: The temporal lobes are vital for emotional processing, including the interpretation of emotional speech cues, recognition of facial expressions, and formation of emotional memories. Sensors TP9 and TP10 help capture brain responses to emotional stimuli, offering insights into how emotional content is processed, whether through auditory or visual cues. 2. AF7 and AF8: Prefrontal Cortex Location: On the forehead, adjacent to the hairline, situated over the prefrontal cortex on both sides. Importance: The prefrontal cortex is key to regulating and controlling emotions. The data from sensors AF7 and AF8 shed light on emotional regulation processes, revealing the mechanisms of emotion expression and management.   Higher resolution with System 10-10 For applications requiring more detailed brain activity mapping, the 10-10 system offers a higher resolution extension of the traditional 10-20 system, doubling the number of electrodes to capture more nuanced electrical patterns of the brain. This enhancement allows for a more granular analysis of cerebral functions and disorders, bridging the gap between broad regional monitoring and specific neural pathway observations. In the 10-10 system, electrode placements are refined using a 10% division scale to introduce intermediate sites between the established 10-20 system locations. This denser grid enables a more precise localization of brain activity, essential for advanced research studies, detailed clinical diagnostics, and neurofeedback applications. The Modified Combinatorial Nomenclature (MCN) introduces additional labeling for these new intermediate positions, expanding the vocabulary of electrode sites. Figure 2: 10–10 system The MCN employs numerical designations (1, 3, 5, 7, 9) to denote percentages of distance across the left hemisphere from the inion to the nasion, adding specificity to the electrode's scalp location. New alphabetic codes delineate areas between traditional 10-20 sites, offering insights into regions previously generalized in broader categories: AF (Anterior Frontal): Situated between the prefrontal (Fp) and frontal (F) regions, providing insights into prefrontal cortex activities that underpin decision-making, social behavior, and personality. FC (Fronto-Central): Located between the frontal (F) and central (C) areas, crucial for motor function control and higher cognitive processes. FT (Fronto-Temporal): Bridges the frontal (F) and temporal (T) regions, key for understanding the integration of auditory information and language processing. CP (Centro-Parietal): Nestled between central (C) and parietal (P) lobes, significant for sensory integration and spatial orientation. TP (Temporo-Parietal): Between temporal (T) and parietal (P) lobes, important for auditory perception, language comprehension, and social cognition. PO (Parieto-Occipital): Lies between parietal (P) and occipital (O) regions, essential for visual processing integration. Additionally, the MCN revises the labeling of some electrodes to align with this expanded framework, renaming T3 to T7, T4 to T8, T5 to P7, and T6 to P8, thereby enhancing the specificity of temporal and parietal monitoring. For even more detailed brain activity analysis, a "5% system" or "10-5 system" has been proposed, further increasing the number of electrodes and potentially offering unprecedented insights into the brain's electrical dynamics. This evolution in EEG electrode placement systems underscores the continual advancement in neurotechnology, striving for a deeper understanding of the brain's complex workings. Naxon Explorer is an affordable, useful tool and neurofeedback system for researchers in Neuroscience, Psychology, Medicine, Engineering and Information Technology. It is a web platform dedicated to exploring brain data taken with portable electroencephalographs (portable EEGs from Interaxon – Muse devices), where both an experienced researcher or recently graduated professional can easily explore the brain. Figure 3: Muse II EEG device from Interaxon   The central part of the platform is displayed where you visualize brain wave data in real time on a graph of voltage and time, divided by channel. Figure 4: Naxon Explorer output for Muse devices   Naxon Emotions is a tool to objectively measure and record a person's emotions and cognitive states in real time and at low cost using portable electroencephalography (EEG) headbands. This real-time emotion recognition system is based on neurophysiological data from EEG, cloud computing and AI. Measuring concentration and alertness: Naxon Emotions can be used to measure and record in real time the state of concentration and alertness of a person. This record can be viewed on the platform or downloaded in an Excel format for further analysis with other tools. The possibilities of using these records are multiple, such as providing support and brain correlates to psychometric measures, evaluating clinical interventions, conducting field research in the area of neuromarketing, among others.   Figure 5 : Naxon Emotions output using Muse devices The Neuphony Desktop Application integrates seamlessly with Neuphony's EEG devices, utilizing electrode placements that are pivotal for analyzing brainwave data effectively. Focusing on electrodes Fp1, Fp2, F3, F4, Fz, and Pz, this application leverages the strategic positioning of these sensors to capture detailed neurological activity and cognitive states, offering a comprehensive view of an individual's cognitive health.   Electrode Placement and Functionality: Fp1 and Fp2 (Pre-frontal): Positioned on the forehead, these electrodes monitor the prefrontal cortex, a region associated with higher cognitive functions, decision-making, and personality. This area's activity is crucial for understanding cognitive states such as concentration and stress levels. F3 and F4 (Frontal): Located on the frontal lobe, these electrodes are essential for assessing cognitive processes related to problem-solving, emotion, and motor function. The frontal lobe plays a significant role in emotional regulation, making these electrodes valuable for studies on mood and affective states. Fz (Frontal Midline): This electrode, positioned at the midline of the frontal lobe, is instrumental in capturing symmetrical brain activity related to cognitive load and attention. It provides balanced insights into frontal lobe dynamics, essential for tasks requiring concentration and focus. Pz (Parietal Midline): Situated at the midline of the parietal lobe, Pz is crucial for processing sensory information and spatial orientation. This electrode's data contribute to understanding how individuals interact with and perceive their environment, influencing cognitive functions like navigation and manipulation of objects.   Utilizing Electrode Data for Cognitive Insights: The Neuphony Desktop Application harnesses the data from these electrodes to offer real-time EEG monitoring and cognitive insights. By analyzing band power across different brain regions, the application can discern patterns related to focus, relaxation, vigilance, and mental fatigue. This is particularly valuable in wellness centers and research settings where understanding the nuances of cognitive states can enhance therapeutic interventions or scientific studies.   Advanced Features for In-depth Analysis: Import/Export of .edf Files: Allows for the integration of brainwave data into broader research frameworks, facilitating longitudinal studies and cross-session analyses. Multiple Experiment Support: Enables diverse studies, from cognitive response tests to sensory processing, leveraging the specific electrode placements for targeted insights. Session Playback and Band Power Analysis: Offers the ability to revisit recorded sessions for detailed examination and understand the spectral content of brainwaves, which is pivotal for recognizing patterns associated with various cognitive states. Real-Time EEG and Cognitive Insights: Provides immediate feedback on neurological activity, enabling dynamic adjustments in therapeutic or research protocols based on observed brainwave patterns. The Neuphony Desktop Application, coupled with strategic electrode placements, represents a powerful tool for advancing our understanding of the brain's intricate workings. By focusing on key areas like the pre-frontal and frontal lobes, and employing advanced analysis features, Neuphony opens up new possibilities for cognitive health research and wellness applications.   Figure 6: Electrodes in Neuphony devices   References: 10–20 system (EEG) https://en.wikipedia.org/wiki/10%E2%80%9320_system_(EEG) Visualizing brain wave data in real time https://naxonlabs.com/blog/visualizing-brain-wave-data-in-real-time Measuring concentration and alertness with Naxon Emotions https://naxonlabs.com/blog/measuring-concentration-and-alertness-with-naxon-emotions Analyzing Brainwaves Data with Neuphony Desktop Application https://naxonlabs.com/blog/analyzing-brainwaves-data-neuphony-desktop-application

Revolutionizing Neurotechnology with Software Development and AI: Naxon Labs' Vision for 2024   Innovating at the Intersection of Neurotechnology and Artificial Intelligence Naxon Labs stands at the forefront of a thrilling evolution as we enter 2024, marking a pivotal moment in our commitment to innovation within the realm of neurotechnology. After years of pioneering work with our flagship products, Naxon Explorer and Naxon Emotions, we are shifting our focus towards harnessing the power of software development and artificial intelligence to push the boundaries of neuroscience research and applications.   The Journey So Far Our journey began with a vision to make neuroscience accessible and actionable through technology. Naxon Explorer and Naxon Emotions were the first steps towards realizing this vision, offering tools for detailed brainwave analysis and real-time emotion recognition. These technologies provided invaluable insights into the complexities of human cognition and emotion, serving as a catalyst for our next leap forward.   A New Era of Neurotechnology The fusion of neurotechnology with software development and artificial intelligence (AI) opens up unprecedented opportunities for advancing our understanding of the brain. Naxon Labs is excited to lead this charge by offering a suite of services designed to empower researchers, clinicians, and innovators in the neuroscience field:   Custom Software Development: We’re dedicated to creating bespoke software solutions tailored to the unique needs of neuroscience research. From sophisticated algorithms for data analysis to intuitive platforms for experimental management, our goal is to enhance the efficiency and impact of neuroscience research. Machine Learning for Neuroscience: By applying machine learning techniques to neuroscience data, we aim to unlock new insights and predictive models that can transform our approach to understanding neural mechanisms and disorders. Data Science Consultation: Our team of experts offers consultation services to guide the collection, preprocessing, and analysis of neuroscience data, ensuring the highest standards of data integrity and scientific validity. Neuroinformatics Solutions: We’re developing comprehensive platforms for data management and analysis, designed to facilitate collaboration, data sharing, and the discovery of novel insights within the neuroscience community. Researcher Training and Support: Recognizing the importance of knowledge transfer, we provide training programs and continuous support for researchers navigating the complexities of new technologies and methodologies in neurotechnology.   Charting the Future Together   The potential of integrating software development and AI with neurotechnology is vast and largely untapped. Naxon Labs is committed to exploring this potential, driven by our passion for innovation and the promise of delivering transformative solutions to the neuroscience community.   We invite researchers, clinicians, and technology enthusiasts to join us in this exciting journey. Together, we can unlock new dimensions of understanding the brain, paving the way for new breakthroughs in neuroscience.

As we navigate through the early days of 2024, it's an opportune time to look back at the significant strides Naxon Labs made in 2023. The year at Naxon Labs was defined by innovative breakthroughs, strategic collaborations, and an expanded global presence that underscored our commitment to advancing neurotechnology. Here’s a recap of our pivotal moments from 2023 and a glimpse into what's on the horizon.   Strategic Partnerships and Innovative Projects Academic Collaborations and Research Initiatives Our partnerships with academic institutions, including Universitat de les Illes Balears, have been instrumental in propelling research and development at the intersection of neuroscience and technology. These collaborations have paved the way for new explorations and discoveries, reinforcing our mission to merge scientific inquiry with technological advancement.   UVJIA: Fostering Interdisciplinary Research The establishment of the Video Games and Artificial Intelligence Innovation Unit (UVJIA) marked a significant step in our journey towards creating a hub for interdisciplinary research. This initiative has not only enriched the academic landscape but also underscored our dedication to exploring the confluence of gaming, AI, and neurotechnology.   Engagements with IIT Mandi iHub and HCI Foundation Our collaboration with IIT Mandi iHub and HCI Foundation highlighted our commitment to innovation in neurotechnology and human-computer interaction. This partnership is poised to unlock new synergies and drive advancements that redefine our interactions with technology.   The Reciprocal Brains Project and Dementia Research Projects like Walid Breidi's Reciprocal Brains and our collaborative research with the University of Aveiro have showcased our efforts in using neurotechnology for artistic expression and social impact, particularly in addressing challenges related to dementia through innovative approaches to reminiscence therapy.   Neurotechnology devices Naxon Labs continued promoting Naxon Explorer and Naxon Emotions which run on top of Muse devices. There are advanced tools that can be used to integrate many technologies, including virtual reality. Also, we have introducing how to explore brain waves with Neuphony products and its software applications.   Expanding Our Global Footprint   Engagements in New Zealand Our activities in New Zealand have played a crucial role in fostering global dialogue on neuroscientific advancements. By getting to know local neurotechnology developments, we've shared insights and absorbed diverse perspectives, enriching our understanding and contributions to the field.   NeuroFrance 2023 in Lyon, France Getting to know the NeuroFrance 2023 conference in Lyon allowed us to connect with global discussions on neuroscience. This engagement not only reinforced our position but also connected us with the international neurotechnology community, capturing knowledge and insights.   Visit to CogLab in Paris Our visit to CogLab in Paris in May 2023 stands out as a highlight of the year. Hosted by Hans and engaging with the vibrant community at CogLab and NeuroTechX Paris, we explored potential collaborations and shared visions for the future of neurotechnology. This visit underscored the importance of community and collaboration in driving forward the field of neurotechnology.   Looking Ahead to 2024   As we move into 2024, Naxon Labs is poised for a year of continued innovation, collaboration, and exploration. Our experiences in 2023 have set a solid foundation for further growth and development. We are committed to enhancing our tools, forging new partnerships, and exploring new avenues that bridge neuroscience with practical applications in everyday life. The advancements of the past year serve as a springboard for the exciting possibilities that lie ahead. With a focus on expanding our global collaborations and continuing to innovate at the intersection of art, science, and technology, we are on a path to redefine the boundaries of what's possible in neurotechnology. Join us as we continue to push the frontiers of discovery and innovation in 2024 and beyond. At Naxon Labs, the future is bright, and we are just getting started.