Get the Headband
May 11, 2020.
Naxon Labs launches Brain to Computer Interface solution to explore the mind: Naxon Explorer
Montevideo, May 11th, 2020.   Naxon Labs, a company working with portable EEG technology to obtain neuronal information based on brain waves, officially launched today Naxon Explorer: a cheap and useful tool and neurofeedback system for professionals in the fields of Neuroscience, Psychology, Medicine, Engineering and Information Technology.   Explorer consists of an electroencephalography monitor adapted for portable EEG (Electroencephalography), in particular the Muse headset by Interaxon Inc.    With Naxon Explorer you can visualize, record and analyze brain activity with wireless EEG technology. It incorporates features to organize projects, clients or participants, at the same time you can attach notes, synchronize events and change parameters during recordings. Using this technology, you can save time with automatic blink and artifact detection and display in real time brain wave frequencies per channel or by average. The data captured can be downloaded for further analysis with tools like MATLAB, Brainstorm or EEG Lab. The device can be connected from a PC, a MAC or a tablet with Bluetooth.     To access the service, get your portable EEG and your account on the platform and you can start using it for USD 14.99 per month (or an annual payment with discount) plus one-time purchase of the hardware (USD 249), al taxes included. For those already owning a Muse headset all you need is the service subscription.    An upcoming update will integrate machine learning tools and automatic pattern analysis for analyzing EEG data and detect the presence of certain evoked potentials based on the events or stimuli marked in a session, and also training models based on input EEGdata that can then be used for practical applications.   “We want to open to the world the possibilities of researching the brain while betting on innovation on what the major current technology leaders agree is the 21st century next frontier: neurotechnology, an area that combines applied neuroscience, wearable technology, BCI, Cybernetics, biosensor development, AI and machine learning” said  the cognitive neuroscientist Leandro Castelluccio, MSc, Naxon Labs’ CEO and Co-Founder.   Through Chevening Scholarships, Leandro got his master in Cognitive Neuroscience in the University of Sussex, a leading research-intensive university located in Brighton, United Kingdom, where he got a lot of interest in leveraging information technology tools to have a better understanding of brain activity in patients. Currently Leandro also develops research activities at the Psychology School at Universidad Católica del Uruguay where he got his bachelor’s degree in psychology.   Explorer comes as a first release as the company works in Emotions, a second platform consisting of an emotion monitoring system that translates brain information into objective visual markers of states such as anxiety, relaxation, concentration, joy or sadness, among others.   About Naxon Labs:   Under the framework of Brain-Computer Interfaces, Naxon Labs is a company that works with portable EEG technology for the development of practical tools and innovative applications for Medicine, Clinical Psychology (Neurofeedback), Educational Psychology, Sleep and Well-being, Brain Research, as well as mind-controlled hardware and software technology.   Naxon’s team of professionals in the areas of cognitive neuroscience, psychology, neurophysiology, medicine, computer engineering and information technology, are realistic and committed to down to earth products that solve current real issues, always betting on novelty and generating differential proposals.   More information about Naxon Labs is available at E-mail: Montevideo, Uruguay
April 17, 2020.
Join the BCI & Neurotechnology Spring School 2020
We’ll be joining one of the most significant BCI Meeting worldwide: the BCI & Neurotechnology Spring School 2020, consisting of virtual talks and keynotes by international experts. 5 days long starting next Monday 20th of April.   You can register for free at Eventbrite BCI.   Some of the keynote speakers:   Paul Sajda (Columbia University USA) Jose Azorin (University of Elche Spain) Milena Korostenskaja (Institute of Neuroapproaches USA) Nuri Firat Ince (University of Houston USA) Dean Krusienski (Virginia Commonwealth University USA) Tomasz M. Rutkowski (RIKEN AIP, University of Tokyo Japan)   Some of the subjects that will be addressed:   -Bci with eeg & other biosignals -Motor rehabilitation with bci -Invasive bci and brain stimulation -Bci with eeg/fnirs and hyperscanning -Brain assessment and unicorn brain interface   For more information go to: link   Naxon
March 30, 2020.
Recent news in Neurotech
Find out the latest news related to neurotech, EEG wearables and neuroscience:  Neurotech in Sports: “The Head Should Always Be in the Game” Neurotechnology & Corporate Wellbeing? Yes, Please! Why we should develop neurotechnology. The brief history of neurotech: the origin of data reading, visualization, and brain stimulation technologies. The definitive guide to neuroexperience The Top Neurotechnology Devices of 2020 Ulster University pioneering ways to improve the treatment of patients with serious brain injuries Epilog – Medical-grade seizure monitoring from the comfort of home New electrodes can better capture brain waves of people with natural hair (Art) Night at the Museum
March 30, 2020.
EEG, Smartphones and Pattern Detection
Brain-Computer interfaces or BCIs are booming, where new equipment and analysis techniques are seen every day for applications development.   Imagine a personal laboratory where, through a simple headband and a smartphone, you can record your brain waves, extract significant patterns automatically and apply that information to the activities or tasks you perform everyday.    In Naxon we are working to make this possible.   Through the combination of EEG sensors with smartphones, computers or tables, we offer new opportunities to obtain daily activity brain records. This is facilitated by the development of tools to interpret data and provide meaningful measures.   We intend to interpret brain activity quickly and efficiently to interact directly with hardware and software. It is common to work with motor activity to move a robotic arm, or to use brain information related to what a person wants to try to communicate without having the need to speak. Those are some of the applications of this type of technology. Currently in Naxon we are developing software that will allow us to obtain significant patterns in an automated way. From link An example is the event related potential "P300", a very studied pattern in academia, where several studies manage to detect it with portable EEG devices. It indicates, among other things, the registration of an unusual stimulus by a person.    Imagine being able to see how your brain responds to stimuli personally, with a laboratory on your smartphone. What until recently was extremely expensive, with the new technology and developments such us our work, we are making it possible for such applications to be available massively.
March 30, 2020.
2019: the year Montevideo started the Neurotechnology Meetup Group
It was November 2019 when we decided to create a Meetup Group in Montevideo focused on neurotechnology and brain computer interfaces (BCI). The aim of the group is to promote, disseminate, update and encourage synergies between of companies related to the field.  The group caught attention, quickly captured +100 members and we ran the first meeting on early December 2019. In Parque Rodo area, Pyxis hosted the inaugural session in its high floor conference room with a very nice view of the park, the city and the river. Neurotechnology and Neurosciences are booming worldwide As host, Diego Sastre, Pyxis CEO, introduced the session giving the context of Pyxisportal initiatives related to wellness and people centered initiatives, which will have development in 2020 promoting ecosystems and synergies between companies. Leandro Castelluccio, psychologist, neuroscientist and Naxon Labs CEO, continued the session and gave an introduction into the topic, the state of the art and the developments being made in Uruguay with global projection. Leandro began by explaining how neuronal signaling uses energy, synapses, how we measure brain activity, different methods in cognitive neuroscience, to finally introduce BCI as a direct communication path between a brain and an external device. Using the Gartner technology maturity model, it was presented the current state of the art and the cases of invasive and non-invasive connections were presented. One of the invasive mechanisms is the one promoted by Elon Musk in his Neuralinkventure. This system faces competition from the Pentagon, which has funded research to develop robotic control systems that allow brain control of the prosthetic devices. Separately, researchers backed by the Defense Advanced Research Projects Agency (DARPA) have managed to create interfaces that allow quadriplegics to manipulate robot arms with skill. Rarely activity can be isolated in regions of the brain, such as the prefrontal lobe and the hippocampus. Then there is the question of translating the neuronal electrical impulses into machine-readable information. Researchers have yet to decipher the coding of the brain. The pulses of the visual center are not like those that occur when formulating the speech, and sometimes it is difficult to identify the points of origin of the signals. In a recent study published in the journal Nature, scientists trained a machine learning algorithm on data recorded from previous experiments to determine how tongue, lips, jaw and larynx movements create sound. They incorporated this into a decoder, which transformed brain signals into estimated movements of the vocal tract and fed them to a separate component that converted the movements into synthetic speech. The neuronal activity in the brain is random: it is stochastic, which means that the neuronal representation at the level of the individual neuron is noisy. This is just one of the reasons why we need to register many neurons to get a high fidelity reading. But Musk projects that the Neuralink system will eventually be used to create what he describes as a "[super smart] [cognitive] digital layer" that allows humans to "merge" with artificially intelligent software. “The restriction is input and output speed. What will ultimately limit our capabilities is bandwidth” In addition to Neuralink, invasive systems such as Neuropace, Paradomics and Synchronwere presented. Then, different non-invasive BCI initiatives were discussed such as Cognixion, CTRL-labs recently acquired by Facebook, BrainRobotics, NextMind, Neurable, Myndlift, Emotiv, Brainlink, Brainco, Bitbrain, Atentiv, Focusband, Starlab, Gtec, Dreem, Neurosky, Muse and the Open BCI initiative. These systems use electroencephalography, an electrophysiological monitoring method to record the electrical activity of the brain. It is typically non-invasive, with electrodes placed along the scalp. It is used in medicine for diagnostic applications.  Through the use of non-invasive electrodes in the scalp, the electric field produced by neurons is captured. Particularly, portable electron encephalography has lower costs, more versatility (it can be used in novel scenarios), the information is easier to read and interpret, has wireless connectivity and is easier to place and use. Exploring the mind and discovering emotions By developing SaaS platforms that work with a specific type of BCI known as portable EEG, Naxon Labs delivers useful information for professionals and consumers in general. Naxon Explorer is an electroencephalography monitor for portable EEG. It is aimed at academics and researchers. It has the advantage of low cost and has integrated AI tools and automatic pattern analysis. Naxon Emotions shows in a simple and objective way states such as anxiety, relaxation, concentration and other emotional states. It provides objective records of patients for diagnosis and treatment, monitoring the evolution of the patient, allows remote monitoring and therapy, and improves the implementation of techniques for the regulation of mental disorders. It has applications in various specific disorders such as attention deficit and phobias. These products use neural networks, multilinear predictive models and well-known brain patterns in academia. Anyone can use it without knowing about neurosciences.
March 30, 2020.
7 Ted talks about Neurotech you must watch
1) In this first talk, Tan Le (founder & CEO of Emotiv, a bioinformatics company), one of the fist Portable EEG Technology Companies, shows us a computer interface that reads its user's brainwaves, making it possible to control virtual objects, and even physical electronics, with mere thoughts (and a little concentration). She demos the headset, and talks about its far-reaching applications. 2) In the following talk Tan Le show brain research that is something typically done in a hospital or lab, taking a look at a patient experiencing some sort of brain irregularity. Tan Le demonstrates how we can take a different approach to better understand the way the brain works in everyday situations. 3) Modern technology lets neuroscientists peer into the human brain, but can it also read minds? Armed with the device known as an electroencephalogram, or EEG, and some computing wizardry, our intrepid neuroscientists attempt to peer into a subject's thoughts. 4) Greg Gage is on a mission to make brain science accessible to all. In this fun, kind of creepy demo, the neuroscientist and TED Senior Fellow uses a simple, inexpensive DIY kit to take away the free will of an audience member. It's not a parlor trick; it actually works. You have to see it to believe it 5) You may remember neuroscientist Miguel Nicolelis — he built the brain-controlled exoskeleton that allowed a paralyzed man to kick the first ball of the 2014 World Cup. What’s he working on now? Building ways for two minds (rats and monkeys, for now) to send messages brain to brain. Watch to the end for an experiment that, as he says, will go to "the limit of your imagination." 6) Two hundred million years ago, our mammal ancestors developed a new brain feature: the neocortex. This stamp-sized piece of tissue (wrapped around a brain the size of a walnut) is the key to what humanity has become. Now, futurist Ray Kurzweil suggests, we should get ready for the next big leap in brain power, as we tap into the computing power in the cloud. 7) As an expert on cutting-edge digital displays, Mary Lou Jepsen studies how to show our most creative ideas on screens. And as a brain surgery patient herself, she is driven to know more about the neural activity that underlies invention, creativity, thought. She meshes these two passions in a rather mind-blowing talk on two cutting-edge brain studies that might point to a new frontier in understanding how (and what) we think.
March 30, 2020.
Naxon participated in the Global Digital Services Summit '19
Check our twitter posts of the event The development of portable systems and applications of brain computer interfaces is at the forefront of technological innovation. Naxon introduces a key area and presents new paradigms to the field of technology. This was demonstrated in the openness and interest we received during the summit. We hope to continue improving our technology and open up business opportunities and knowledge sharing.   The objective of the forum in which we participated is to provide an opportunity for participants to exchange the latest trends in the global services sector and identify business and investment opportunities for SMEs in Latin America and the Caribbean. More than 600 companies participated in the event.   As mentioned at mitagds, "the exponential acceleration of digital transformation presents a challenge and an opportunity for governments and companies in the Latin American and Caribbean region to unite to improve the lives of their citizens and the competitiveness of their economies ... " Watch a video of the event. "...I think what's gonna happen tomorrow with Uber, you never now... In my view you will not even gonna be using a mobile phone by the way, to call a taxi. You will just think about it, validate it and it will come. Naxonlabs here in the city itself is writing or doing something on mapping the brain waves and analyzing it. So companies like that will make the Uber experience even different within the 2-3 years itself..."   Global Digital Services Summit Buenos Aires - Avinash Vashistha (Chairman & CEO Tholons)   During the event we had the opportunity to establish contacts with various potential partners and investors, generating a first instance of dialogue for the development of common projects, opening the possibility of applying our technology to various areas of interest, at the same time as we get involved in other markets that generate new possibilities. Several projects were discussed and some are already underway.   Check our twitter posts of the event
March 30, 2020.
The use of portable EEG technology in sleep disorders
One of the areas of gradual development of Naxon refers to applications that evaluate the quality of sleep and can be used personally and individually by users of portable EEG devices and professionals in the areas of Clinical Psychology and Sleep Medicine. The advantages of applying this technology to this area are multiple, for example, remote monitoring and better patient follow up. At Naxon, through the development of our integrated software platform with pattern analysis tools, we seek one of the applications to be the staging of sleep and the completion of preliminary analysis of the person's sleep quality. Below we show the current problems in the area and how this new technology is introduced to it.  Sleep disorders in today's world Sleep disorders are one of the most relevant health problems currently. The importance of a good quality of sleep is not only fundamental as a determinant factor of health, but as an element that promotes a good quality of life (Sierra, Jiménez-Navarro, & Martín-Ortiz, 2002). Sleep disorders refer to a wide variety of conditions, such as insomnia disorders, sleep-related breathing disorders, central disorders of hypersomnolence, circadian sleep-wake rhythm disorders, sleep-related movement disorders and parasomnias, among others (Sateia, 2014). In a sample from the United States, a general prevalence of current or previous sleep disorders in adults of 52.1% was found. Specifically, the results show a prevalence of insomnia of 42.5%, 11.2% of nightmares, 7.1% of excessive sleep, 5.3% of somnolence and 2.5% of sleepwalking. These conditions were often chronic and usually started at an early age (Bixler, Kales, Soldatos, Kales, & Healey, 1979). The sleep problems of older people are different from those of younger people. Sleep apnea and periodic leg movements (PLMS) during sleep are extremely common in older people. Complaints of insomnia, snoring, daytime sleepiness and lack of sleep, which are also very common in this population, should not be taken lightly, as they can be symptoms of these physiological sleep disorders. Also, subjective reports of lack of sleep, insomnia, snoring and excessive daytime sleepiness should not be taken lightly and should not be assumed to be a normal sign of aging (Ancoli-Israel, 1989). It has been estimated that among people over 65 years of age, half of those who live in their own homes and 60% of those who live in residences suffer from a sleep disorder, of which , the difficulty to start and maintain sleep and Daytime sleepiness are more common. On the other hand, in the United States, 25 million prescriptions of hypnotics are extended every year and 40% of them are used by the elderly. In the case of Spain, 58% of people who consume hypnotics are elderly and, as is observed in other countries, the recommendations for their use are incorrectly followed, thus, drugs with intermediate or long-term action are used inappropriately. (Benetó, 2000). On the other hand, approximately 25% of all children experience some type of sleep problem at some point during childhood. Several studies have examined the prevalence of sleep complaints reported by parents and children in large samples of healthy children and adolescents and with typical development, many of these have also delineated the association between interrupted sleep and behavioral concerns. Sleep problems are even more frequent in children and adolescents with chronic medical conditions, neurological and psychiatric development issues (Owens, 2007). Even in healthy children, a prevalence of 37.4% of sleep disorders was found. 40.7% of these children have problems such as resistance to sleep/fear of sleeping alone and between parasomnias, nightmares and night terrors are present (Convertini, Krupitzky, Tripodi, & Carusso 2003). On the other hand, syndromes such as attention deficit hyperactivity disorder (ADHD) and sleep disorders have a high correlation and are very frequent in a neuropediatric clinic. By improving the sleep of these children, the symptoms of ADHD diminish and the administration of psychostimulants whose undesirable side effects generate great anxiety in the parents of these children is avoided (Fursow, Betancourt, & León 2006). On the other hand, several studies have shown the high prevalence of sleep disorders among adolescents. As a cause, psychological, hormonal, puberal factors and inadequate sleep habits are invoked. In a sample of 1,155 students, 38.55% reported poor quality sleep, 23.1% difficulty in conciliation, 38.2% night awakenings and 15.9% early awakening. 17.7% reported a complaint of sleep plus some symptom of insomnia; the prevalence of insomnia found was 9.9%. Snoring (20.5%), Somnilochia (45.4%) and nightmares (29.5%) are the most frequent parasomnias. Additionally, 53% complain of excessive daytime sleepiness (Jiménez et al., 2004). In university students, certain results show that approximately 30% have poor sleep quality, excessive latency and poor sleep efficiency; no differences were found between men and women in any component, with the exception of hypnotics use, where women had a higher score (Sierra, Jiménez-Navarro, & Martín-Ortiz, 2002). In addition, hospitalized adult patients often have sleep problems. Most studies have focused mainly on patients in the intensive care unit. However, many medical and surgical disorders can alter the normal architecture of sleep (Aguilera Olivares, Díaz, & Sánchez, 2012). The direct and indirect costs of sleep disorders are high. An analysis of this was carried out in Australia. The total financial cost (independent of the cost of suffering) of $ 4524 million represents 0.8% of the Australian gross domestic product. The cost of suffering of $ 2.97 billion is 1.4% of the total burden of the disease in Australia (Hillman, Murphy, Antic, & Pezzullo, 2006). Polysomniography The study of sleep in the laboratory is carried out through the polysomnographic record (PSG), which consists of the simultaneous recording of the EEG, the electrooculogram (EOG), the electromyogram (EMG) of the submental muscles, the electrocardiogram (ECG) and breathing. In addition, other parameters can be monitored: rectal temperature, blood pressure, limb movements, blood gases, endosophageal pressure, penile erection and electrodermal reaction (Peraita-Adrados, 2005). PSG measures the cycles and stages of sleep, either rapid eye movement (REM) sleep or synchronized sleep, or non rapid eye movement (NREM) sleep or desynchronized sleep, which has three stages that can be detected by means of the brain waves (EEG). Polysomnography can be carried out both in a specialized sleep clinic and in the person's own home. At a specialized center, you are asked to arrive approximately 2 hours before bedtime. The person will sleep in a bed in this center. The exam is often done during the night. Electrodes are placed on the chin, scalp and on the outer edge of the eyelids. There are monitors to record the heart rate and breathing attached to the chest, which remain in place while you sleep. The electrodes record signals while one is awake (with closed eyes) and during sleep. In the exam, the amount of time it takes to fall asleep is measured, as well as the time it takes to enter the rapid eye movements sleep. A trained specialist observes while one is asleep and notes any change in heart or respiratory rate. There are also monitors that record movements during the session. Sometimes a camcorder records movements during sleep. Due to the particular circumstances of doing it in a specialized center, with all the monitors and register equipment required, users usually report some difficulty to be able to fall asleep. In the case of doing it at home one can use a polysomnograph to help diagnose sleep apnea. In a sleep center the device is picked up or a specialized therapist comes to your home to prepare that device. Home exams may be used if the sleep specialist thinks that you have obstructive sleep apnea and there are no other sleep disorders or other serious problems, such as heart disease or lung disease. Portable EEG applications Several studies have ventured into testing new technologies similar to portable EEG devices with smaller electrodes to perform sleep studies, obtaining very good results. There have been numerous studies of portable monitors in the last two or three decades. The US government and medical societies have extensively reviewed this literature several times in an attempt to determine whether portable monitoring should be used more widely to diagnose disorders such as obstructive sleep apnea (OSA), for example. It is anticipated that portable monitoring as a diagnostic modality for OSA for example will be used more frequently. Physicians and others who consider the use of portable monitors should fully understand the advantages and limitations of this technology (Collop, 2008). A couple of years ago, portable PSG was already being tested. Mykytyn, Sajkov, Neill and Mc Evoy (1999) compared the validity of the portable polysomnographic recorder with a laboratory-based polysomnographic system. The quality of the signals was comparable between the PSGP and PSGL studies. There was no significant discordance between PSGP and PSGL in the final diagnostic formulations, concluding that portable polysomnography is a viable alternative to laboratory-based polysomnography and can be further improved by a better sensor connection. Fry, DiPhillipo, Curran, Goldberg and Baran (1998) evaluated the unattended complete PSG registered in the home and assessed the ability to acquire, store and analyze polysomnographic data. Using a digital recording system with miniature preamplifiers and the ability to record 18 channels of polysomnographic data, they found that full PSG without supervision can be performed at home with reliable and high quality recordings. The full PSG can be extended to a larger patient population, because it is no longer limited by the number of beds, and there is a reduction in costs due to the elimination of night staff and the cost of installation. More recently, Rosen et al. (2012) conclude that a strategy in the home for diagnosis and treatment compared to PSG in the laboratory was not inferior in terms of acceptance, compliance, treatment time and functional improvements. According to Lucey et al. (2016), a precise study on home sleep to assess sleep stages based on EEG and EEG potency would be advantageous both for clinical and research purposes, such as longitudinal studies that measure changes in sleep stages of the EEG over time. According to the authors, given that even limited PSG to monitor only sleep stages can be prohibitive for finances, unattended sleep monitoring that includes sleep stages and EEG potency but does not require placement of sensors by a technician would have an important clinical and scientific application for longitudinal research studies and clinical trials. The staging of sleep and the measurement of spectral power with a single-channel EEG recorded on the forehead, as the Neurosky device has for example, presents multiple challenges compared to PSG that may affect the expected results. First and most obvious, the forehead canal will not detect an alpha rhythm and it is predicted that this will limit the evaluation of wakefulness and stage N1. Secondly, the waveforms used for sleep staging differ in the topographic distribution. The K complexes, for example, are distributed mainly over the mid-frontal branches, while the delta waves reach their maximum in both the frontal and occipital areas. In addition, the sleep spindles have a bimodal anterior-posterior distribution and the single EEG will allow the revision of the anterior sleep spindles. Also, age-related changes in spindle density, amplitude and duration are greater anteriorly. Thirdly, there are limitations on the ability of the single EEG to assess the topographic differences in slow wave activity (SWA) reported in adults or the subsequent change to previous SWA maximum activity reported during childhood. The NREM power also undergoes an anterior-posterior change during consecutive periods of NREM sleep that can not be assessed with a single forward lead. In addition, the measurement of SWA during REM may be less accurate than in a central derivation in a PSG due to eye movements (Lucey et al., 2016). However, the results of these authors show that the single-channel EEG provides results comparable to polysomnography in the evaluation of REM, combined sleep stages N2 and N3, and several other parameters, including the activity of slow forward wave. The data establishes that the single-channel EEG can be a useful research tool. Sleep Staging and portable EEG: new data processing techniques The staging of sleep involves the recording of the different stages of sleep and is part of the study of polysomniography, although a staging can also be carried out by means of an EEG device outside of a standard polysomniography, which usually involves the multiple additional records mentioned above (air flow in and out of the lungs during breathing, oxygen levels in the blood, body position, electrical activity of the muscles, heart rate, etc.). Staging allows us to have a first view of whether there may be problems at the level of sleep. Several works have developed the possibility of automating the staging of sleep through various techniques. This suggests a great possibility to generate software applications that use portable EEG and that can be massively used remotely to evaluate basic aspects of sleep quality to allow the improvement of diagnosis and treatment, as well as generating applications that train people in hygiene of sleep. The automatic classification of sleep is essential due to the fact that, conventionally, doctors have to visually analyze a large volume of data, which is onerous, time-consuming and prone to errors. Therefore, there is a great need for an automated sleep-staging scheme (Hassan & Bhuiyan, 2016). According to Principe, Gala and Chang (1989), the staging of sleep represents a problem of medical decision. The authors developed a model for automatic sleep staging by combining signal information, human heuristic knowledge in the form of rules and a mathematical framework. Liang, Kuo, Hu, Pan and Wang (2012) have proposed an automatic sleep scoring method that combines multiscale entropy (MEE) and autoregressive (AR) models for single-channel EEG to evaluate the performance of the method comparatively with the manual scoring in complete polysomnograms. Their results show that MEE is a useful and representative feature for sleep staging. It has high precision and good applicability for home care because only one EEG channel is used for sleep staging.  In addition, Liang, Kuo, Hu and Cheng (2012) developed an automatic rule-based sleep staging method with an average precision and a kappa coefficient that reached 86.68% and 0.79, respectively. The algorithm can be integrated with a portable PSG system for home sleep assessment. On the other hand, Koley and Dey (2012) worked on the automatic identification of various stages of sleep, such as sleep stages 1, 2, slow wave sleep (sleep stages 3 and 4), REM sleep and wakefulness for single-channel EEG signal. The automatic punctuation of the sleep stages was carried out with the help of a pattern recognition technique that involves the extraction of characteristics, selection and finally classification. The agreement of the estimated sleep stages with those obtained by experts for all sleep stages of the training data set was 0.877 and for the independent test data set it reached 0.8572. The proposed method could be used as an efficient and cost-effective way to stage sleep with the advantage of reducing stress and the burden imposed on the subjects.  On the other hand, Bajaj and Pachori (2013) applied a new method for the automatic classification of the sleep stage based on the time-frequency image (TFI) of the EEG signals. The results show good effectiveness of the proposed method for classifying the stages of sleep from EEG signals. Also, Hassan and Bhuiyan (2016) proposed an automated sleep scoring method based on single channel EEG. A new technique of signal processing is used, the "TQWT" for the staging of sleep. The effectiveness of the method is confirmed by statistical analysis. The performance of the proposed scheme, compared to the existing ones, is promising. The proposed scheme will relieve the burden on physicians, accelerate the diagnosis of sleep disorder and accelerate sleep research. Other advances have also been achieved regarding the nature of the EEG signals, which are are non-linear and non-stationary. It is difficult to perform sleep staging through visual interpretation and linear techniques (Acharya, Chua, Chua, Min, & Tamura, 2010). Therefore, the authors use a non-linear technique, the higher-order spectra (HOS), to extract information hidden in the EEG signal of the dream. Their results indicate that the proposed system is able to identify the stages of sleep with an accuracy of 88.7%. References Acharya, U. R., Chua, E. C. P., Chua, K. C., Min, L. C., & Tamura, T. (2010). 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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. 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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.