Get the Headband
March 30, 2020.
Improving quality of life through portable EEG devices: advances and challenges
Our work at Naxon for professionals aligns with global standards that involve promoting well-being and human development. The advantages of the technology of portable EEG devices applied to the area of Medicine and Clinical Psychology are substantial and significantly improve several current problems, as will be described below. The rapid advances in the use of software technology for the analysis of patterns and the conformation of information processing algorithms allow us to work with these devices generating automatic processing of information that reduces the workload of professionals, increases their efficiency and improves the diagnoses and consequent treatments. Following authors Mirza et al. 2015, improving the quality of life of the elderly and disabled and providing them with adequate care at the right time is one of the most important roles we must play as responsible members of society. Before a few decades, people with disabilities could not perform daily tasks such as turning on the light, making a phone call and even controlling the TV. Disability was an obstacle, so people with impairments needed supervision and daily services. With the evolution of computing power and progress in neural studies, neurotechnology and cybernetics, modern technology and science is managing to overcome human disability (Rihana, Azar, & Bitar, 2016). A recent development within the area of wearable technology and brain-computer interfaces (BCI) has been the creation of portable EEG devices that connect to a computer, tablet or smartphone via Bluetooth, which can really expand the development of applications given this connectivity, as well as the low cost they present, added to the non-invasive qualities of the devices. These headsets use a biosensor that allows capturing brainwaves which can be used to develop an app for mind-controlled hardware as well as other applications such as Neurofeedback training therapy, meditation, managing sleep, assessing emotions, among others. Modern BCI applications have proven to be efficient in "thought controlled devices". A typical example is found in a wheelchair controlled by thought, which uses captured signals from the brain and eyes and processes them to control its movements. The EEG technique deploys an electrode cap that is placed on the user's scalp for the acquisition of signals that are captured and translated into movement commands that in turn move the wheelchair (Mirza et al., 2015). Many EEG commands can be used. For example, the low-cost portable system has been designed and tested to allow a person to control the television through blinking with high precision. A prototype did not exceed 60 usd (Rihana, Azar, & Bitar, 2016). In another context, in the intensive care unit, or during anesthesia, patients are connected to monitors by means of cables. These cables obstruct the nursing staff and prevent patients from moving freely in the hospital. However, it is expected that rapidly developing wireless technologies will solve these problems, one of which is portable EEG (Paksuniemi, Sorvoja, Alasaarela, & Myllyla, 2006). The ability of telemonitoring in relation to software applications is a key feature of these devices, and is defined as the use of information technology to monitor patients remotely. The review of the literature suggests that the most promising applications for telemonitoring are chronic diseases. The monitoring allows to reduce the complications of chronic diseases thanks to a better follow-up; provides health care services without using hospital beds; and it reduces the patient's travel, time away from work and general costs. Several systems have proven to be profitable. Telmonitoring is also a way to respond to the new needs of home care in an aging population. The obstacles to the development of telemonitoring include the initial costs of the systems, the doctors' licenses and the reimbursement (Meystre, 2005). A key feature of portable EEGs are their relatively low costs, even compared to the simpler new hardware used by many clinics today. Recently, the versatility of this EEG technology has been tested, managing complicated tasks with only mind control, such as playing a tactical video game. In this particular case, reliable control is achieved by adapting a steady state evoked visual potential classifier (SSVEP), a type of measurement obtained with EEG, to be robust enough to cope with signal quality of the portable EEG device used, in this case the Emotiv Epoc. The difference in the performance of the game that runs on more advanced EEG equipment of the "research" type compared to the EPOC Emotiv has been examined, having a satisfactory response from the public (van Vliet et al., 2012). Conventional brain-computer interfaces are often expensive, complex to operate, and lack portability, which limits their use in laboratory settings for example. Portable and cheap BCIs can mitigate these problems, but their performance must be proven. Several authors have conducted promising research in this regard. McCrimmon et al. (2017) developed a low cost portable BCI with performance comparable to conventional BCIs. These platforms are suitable for BCI applications outside of a laboratory. According to Krigolson, Williams, Norton, Hassall and Colino (2017), the validation of the use of low cost EEG systems has focused on the continuous recording of EEG data and/or the replication of EEG configurations of large systems that depend on event markers to allow the examination of brain events related potentials (ERP). These authors demonstrate that it is possible to perform an ERP research without relying on event markers using a portable Muse EEG system and a single computer. His work emphasizes that with a single computer and a portable EEG system such as Muse, ERP research can be easily conducted, thus extending the possible use of the methodology to a variety of novel contexts. At the same time, it shows the promising capabilities of these devices. In turn, Armanfard, Komeili, Reilly and Pino (2016) proposed a practical approach to machine learning for the identification of mental vigilance failures using portable EEG signals recorded from a very sparse electrode configuration with only 4 channels. This is a difficult problem since these four electrodes are easily contaminated with flickers and muscle artifacts. The performance of the algorithm based on the proposed machine learning is demonstrated in a real world scenario in which the surveillance lapses are identified with approximately 95% accuracy, adding greater validation to these eeg headsets. On the other hand, the scientific community is working with several tools for the processing and classification of signals, which helps to develop additional applications. Karydis, Aguiar, Foster and Mershin (2015) propose a new autocalibration protocol that can solidly classify brain states when combined with five standard machine learning algorithms. Their results indicate that the portable EEG sensors available in the market provide sufficient data fidelity at sufficiently fast speeds to differentiate in real time the arbitrary states defined by the user. The classification of EEG signals is an important task in BCI. Zhang, Liu, Ji and Huang (2017) present two combined strategies of feature extraction in EEG signals using autoregressive coefficients and approximate EEG entropy of decay signals in subbands, which has also very satisfactory results. An important aspect in the non-invasive research of the brain-computer interface is to acquire the EGG in an adequate manner. From the perspective of the final user, this means with maximum comfort and without any additional inconvenience (for example, washing the hair), while from a technical perspective, the quality of the signal must be optimal for the BCI to function effectively and efficiently (Pinegger, Wriessnegger, Faller, & Müller-Putz, 2016). These authors found that the construction of a reliable BCI is possible with all the systems assessed, and it is the responsibility of the user to decide which system best meets the given requirements. Thus, the brain-computer interface has been investigated to a greater extent in the area of robotics and medicine, and not so much in direct applications to the consumer. As processors have advanced in terms of a surprisingly high performance/cost ratio, BCIs for popular use outside the medical environment has taken great strides to reach consumers in the last decade (Chu, 2017). In this sense McKenzie et al. (2017) evaluated the ability of an smartphone based EGG application, the Smartphone Brain Scanner-2 (SBS2), to detect epileptiform abnormalities compared to the standard clinical EEG. Epileptiform discharges were detected in 14% of SBS2 and in 25% of standard EEG. Despite limitations in sensitivity, SBS2 can become a viable support test for the capture of epileptiform anomalies and extend access to EEG to new populations, especially of limited resources, at a reduced cost. All this research shows the potential to develop cheap software applications that can be used with low-cost personal devices that can be used massively by the population, expanding the accessibility of diagnoses and treatments. References Armanfard, N., Komeili, M., Reilly, J. P., & Pino, L. (2016, May). Vigilance lapse identification using sparse EEG electrode arrays. In Electrical and Computer Engineering (CCECE), 2016 IEEE Canadian Conference on (pp. 1-4). IEEE. Chu, N. N. (2017). Surprising Prevalence of Electroencephalogram Brain-Computer Interface to Internet of Things [Future Directions]. IEEE Consumer Electronics Magazine, 6(2), 31-39. McCrimmon, C. M., Fu, J. L., Wang, M., Lopes, L. S., Wang, P. T., Karimi-Bidhendi, A., ... & Do, A. H. (2017). Performance Assessment of a Custom, Portable, and Low-Cost Brain–Computer Interface Platform. IEEE Transactions on Biomedical Engineering, 64(10), 2313-2320. McKenzie, E. D., Lim, A. S., Leung, E. C., Cole, A. J., Lam, A. D., Eloyan, A., ... & Bui, E. (2017). Validation of a smartphone-based EEG among people with epilepsy: A prospective study. Scientific reports, 7, 45567. Meystre, S. (2005). The current state of telemonitoring: a comment on the literature. Telemedicine Journal & e-Health, 11(1), 63-69. Mirza, I. A., Tripathy, A., Chopra, S., D'Sa, M., Rajagopalan, K., D'Souza, A., & Sharma, N. (2015, February). Mind-controlled wheelchair using an EEG headset and arduino microcontroller. In Technologies for Sustainable Development (ICTSD), 2015 International Conference on (pp. 1-5). IEEE. Karydis, T., Aguiar, F., Foster, S. L., & Mershin, A. (2015, July). Self-calibrating protocols enhance wearable EEG diagnostics and consumer applications. In Proceedings of the 8th ACM International Conference on Pervasive Technologies Related to Assistive Environments (p. 96). ACM. Krigolson, O. E., Williams, C. C., Norton, A., Hassall, C. D., & Colino, F. L. (2017). Choosing MUSE: Validation of a low-cost, portable EEG system for ERP research. Frontiers in neuroscience, 11, 109. Rihana, S., Azar, T., & Bitar, E. (2016, October). Portable EEG recording system for BCI application. In Biomedical Engineering (MECBME), 2016 3rd Middle East Conference on (pp. 80-83). IEEE. Paksuniemi, M., Sorvoja, H., Alasaarela, E., & Myllyla, R. (2006, January). Wireless sensor and data transmission needs and technologies for patient monitoring in the operating room and intensive care unit. In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the (pp. 5182-5185). IEEE. Pinegger, A., Wriessnegger, S. C., Faller, J., & Müller-Putz, G. R. (2016). Evaluation of different EEG acquisition systems concerning their suitability for building a brain–computer interface: case studies. Frontiers in neuroscience, 10, 441. van Vliet, M., Robben, A., Chumerin, N., Manyakov, N. V., Combaz, A., & Van Hulle, M. M. (2012, January). Designing a brain-computer interface controlled video-game using consumer grade EEG hardware. In Biosignals and Biorobotics Conference (BRC), 2012 ISSNIP (pp. 1-6). IEEE. Zhang, Y., Liu, B., Ji, X., & Huang, D. (2017). Classification of EEG signals based on autoregressive model and wavelet packet decomposition. Neural Processing Letters, 45(2), 365-378.
March 30, 2020.
Experience using Muse as a neurofeedback device
By Leandro Castelluccio, MSc - CEO Naxon. There is an increasing innovation in the clinical psychological field and in the area of personal wellbeing in general, driven by the incorporation of important technological advances that have a significant impact in various disciplines within health. A recent case for example, has been the considerable progress in the incorporation of technologies such as virtual reality for the psychological treatment and management of phobias and situations of stress and anguish, to such an extent that it has become an important ally of professionals to improve the quality of life of patients. For those who are not aware of another recent development, Brain-Computer Interfaces or BCIs are having a similar impact, although we are just beginning to explore their applicability for professionals in health. A BCI represents a direct communication path between an improved or connected brain and an external device. It can be invasive in the form of electrodes implanted directly in the brain or non-invasive through an electroencephalogram or EEG, for example, which reads the electrical brain waves in our scalp that result in commands executed by an external hardware and software. A recent development in BCI has been the creation of portable EEG devices that connect to a computer, tablet or smartphone through Bluetooth, which can really expand the development of applications given this connectivity, as well as the low cost they present, added to the non-invasive qualities. Among these devices is Muse developed by the Canadian company Interaxon. It is marketed as a device for anyone who wants to train relaxation and mindfulness. It is also marketed for mental health professionals to work on these aspects linked to the relaxation response and mindfulness. (A person using Muse for meditation, image taken from link) By reading the brain waves produced by the brain, the Muse can detect if the levels of attention and relaxation increase or decrease during a meditation session. Through a system of feedback based on different environmental or musical sounds, the device translates neuronal information into useful signals for the person: if the ambient sound increases in intensity the person is less relaxed and focused and vice versa.  Information that one can find in the Muse application As part of my work at Naxon, where we are developing practical applications with portable EEG devices, I had the opportunity to test for a while the use of the original Muse and Muse 2 to train meditation. One of the points to highlight given my personal experience is that the use of the device helps a lot to encourage the daily practice of meditation and promotes a positive feedback to get involved with this healthy practice. In turn, the Muse provides a detailed record of your meditation activity that allows one to keep track of the evolution of the practice and how the training impacts over time in our anxious responses and in the speed and effectiveness by whcih we managed to induce a relaxation response. In general, the experience has been very comforting and I believe that it has a very good potential in clinical practice for health professionals, for the reasons described above, since it allows the professional to have an accurate record of certain cognitive-emotional responses related to stress and relaxation, and at the same time it represents a tool to work on different techniques in sessions with clients to control anxiety and stress. In turn, unlike a more traditional approach, the use of a novel technological tool allows the client to be more involved and also to monitor a practice remotely that the client can perform at home with the Muse device of the therapist. This record is also very useful for having an accurate measurement of the particular training of a person in meditation, a measure that is very important when conducting scientific studies involving novice individuals or experts in practice. The first model of the device uses brain wave reading to work on meditation. The renewed Muse 2 also offers a measure of heart frequency, the activity of our breathing and the movements of our body. With these new signals, new types of meditations based on these measures can be performed, which make excellent anxiety and stress response markers, which provide even more assistance for professionals, as well as for users who wish to start the practice of meditation or want to improve their experience with it.  
March 30, 2020.
Brain-Computer Interfaces - The new technology on the rise
In July 2017, under the code name "Naxon", exchanging ideas from the neuroscientific and information technology perspectives, the initiative to work with portable EEG technologies emerged with the aim of developing practical and economic solutions for some of the problems present in the areas of Health and Well-being. Maturing the idea during 2018 we began to experiment with Neurosky and Muse devices, and the first stage of development of the venture was conceptualized. A Brain-Computer interface or BCI is a direct communication pathway between an enhanced or wired brain and an external device. These can be invasive in the form of electrodes implanted directly on the brain or non-invasive through an EEG for example, that reads the electrical brain waves on our scalp that are translated into commands that an external hardware and software executes.  You can see the following video for an introduction on the subject: link As shown in the video, BCI can be used for a variety of things, such as helping people with physical disabilities to adapt better through the use of a robotic hand controlled by the mind for example, or use BCI in gaming to execute different commands, or to control other external hardware, like a drone (see the following video). Apart from these example, the technology allows for many applications in different fields the have not yet been explored. Examples of portable EEG devices include: Emotiv Insight, Neurosky and Muse. One of the basic elements of brain functioning lies in the synapse. This is the point where electrical signals from one neuron generate electrical effects in another. The initiation of an action potential, for example, that which allows transmitting nerve impulses, and which is the basis of brain functioning at the level of the neuron, requires the opening of sodium ion channels in the mound of the axon of the neuron. At this level, the phenomena depend on physical and chemical properties and on the characteristics of the biological structures at play. To unleash an action potential, a certain trigger threshold must be overcome thanks to the action of graduated potentials that are added temporally and spatially, provided by the connections with other neurons. In order for activity to occur in the latter, the same phenomena must occur (Kandel, Schwartz, & Jessel, 2001). These EEG devices exploit this electrical property of the brain. Neurons signal electrically. The potential (voltage) across their cell membranes changes, when channels in the membrane open and ions move down their electrochemical gradients. Synapse (image taken from: link) There are different ways of measuring brain activity, all can be classified according to temporal resolution, spatial resolution and invasiveness. (Figure adapted from Churchland & Sejnowski, 1988) The classical electrophysiological monitoring method records electrical activity of the brain, which is mainly noninvasive, with electrodes placed along the scalp, although invasive electrodes are sometimes used such as in electrocorticography. Associated to the use of EEG, we have event-related potentials which investigate potential fluctuations time locked to an event like stimulus onset or button press. Also, spectral content of EEG: analyses the type of neural oscillations (popularly called "brain waves") that can be observed in EEG signals in the frequency domain. EEG reflects large-scale neural activity, producing tiny electrical dipoles which transduce through the skull. However, this electrical activity is incredibly small and must be amplified thousands of times.   Besides research use, EEG is usually used in clinical contexts for diagnostic applications such as in the case of epilepsy, which causes abnormalities in EEG readings. Also in sleep disorders, depth of anesthesia, coma, encephalopathies, and brain death.  It is also a first-line method of diagnosis for tumors, stroke and other focal brain disorders, but this use has decreased with the advent of high-resolution anatomical imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT).  Despite limited spatial resolution, EEG continues to be a valuable tool for research and diagnosis. It is one of the few mobile techniques available and offers millisecond-range temporal resolution which is not possible with CT, PET or MRI.Derivatives of the EEG technique like evoked potentials (EP), are useful, which involves averaging the EEG activity time-locked to the presentation of a stimulus of some sort (visual, somatosensory, or auditory). Event-related potentials (ERPs) on the other hand, refer to averaged EEG responses that are time-locked to more complex processing of stimuli; this technique is used in cognitive science, cognitive psychology, and psychophysiological research. ERPs can be collected across the lifespan. This has implications for obtaining early and objective rather than late and subjective measures of sensory and cognitive sensitivity. Participants do not have to be actively listening to record meaningful auditory ERP for example. This has implications for assessing neural activity in the resting brain, coma and other such states. Auditory (and visual) ERPs can also be assessed under varying conditions of task load and intention. This might help us uncover memory and attention processes. The new portable devices typically have fewer electrodes, but have the advantage of rapid setting, without the need to use gels and be on a lab, are much cheaper, they can be used in different settings and connect wirelessly to a computer device for example.  Some of these devices, like we previously mentioned are the Emotiv, Neurosky and Muse. The Muse device for example is sold as a meditation tool together with a mobile application that gives one auditory feedback of one's state of mind, giving us indications of when we are calm or mind wandering and more anxious.  (A person using Muse for meditation, image taken from link) Upon a review of the recent academic literature on the subject, on can find multiple studies that validate the use of portable EEG for various purposes. Recent advances in the area include the creation of Neuralink, a brain electrode firm by entrepreneur Elon Musk, that seeks to develop BCI. Also, Facebook has recently started developing in the area, focusing on BCI for typing and skin hearing. An area that is experimenting with BCI is virtual reality (VR), where people are working on controlling VR settings with the mind. The precision of these devices is growing, to the point that low-cost EEG can now be used to reconstruct images of what you see. References Churchland, P. S., & Sejnowski, T. J. (1988). Perspectives on cognitive neuroscience. Science, 242(4879), 741-745. Kandel, E. R, Schwartz, J. H. & Jessel, T. M. (2001). Principios de Neurociencia. Madrid: McGraw-Hill.
March 16, 2020.
Naxon successfully secured funding from ANII – “National Research and Innovation Agency”
During the beginning of 2019 we met with our ANII project executive to discuss the details related to the funding we obtained from the agency. ANII is the most important institution in Uruguay in terms of research funding and support of innovative projects and startups in the academic-scientific, technological and entrepreneurial fields. We are very happy and grateful that our proposal has been received with enthusiasm. During the end of 2018 and the beginning of 2019 we had been working on the application for funding at this agency within the line “Tools for Innovation”. On May 21st, the ANII evaluation committee approved our project, with a very positive feedback regarding the interest generated and the prospects for future work. Based on our previous work and the private investment with which the initiative already counted, we have been able to make considerable progress in the products we are developing. This new impulse by ANII implies increasing advances and a promising future.