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Electroencephalography (EEG) has long been used as a versatile and noninvasive diagnostic tool in epilepsy. With the advent of digital EEG, more advanced applications of EEG have emerged. Compared with technologically advanced practice in focal epilepsies, the utilization of EEG in idiopathic generalized epilepsy (IGE) has been lagging, often restricted to a simple diagnostic tool. In this narrative review, we provide an overview of broader applications of EEG beyond this narrow scope, discussing how the current clinical and research applications of EEG may potentially be extended to IGE. The current literature, although limited, suggests that EEG can be used in syndromic classification, guiding antiseizure medication therapy, predicting prognosis, unraveling biorhythms, and investigating functional brain connectivity of IGE. We emphasize the need for longer recordings, particularly 24-h ambulatory EEG, to capture discharges reflecting circadian and sleep-wake cycle-associated variations for wider EEG applications in IGE. Finally, we highlight the challenges and limitations of the current body of literature and suggest future directions to encourage and enhance more extensive applications of this potent tool.

Driven by an increasing number of connected medical devices, Internet of Medical Things (IoMT), as an application of Internet of Things (IoT) in healthcare, is developed to help collect, analyze, and transmit medical data. During the outbreak of a pandemic like COVID-19, IoMT can be useful to monitor the status of patients and detect main symptoms remotely, by using various smart sensors. However, due to the lack of emotional care in the current IoMT, it is still a challenge to reach an efficient medical process. Especially under COVID-19, there is a need to monitor emotional status among particular people like the elderly. In this work, we propose an emotion-aware healthcare monitoring system in IoMT, based on brainwaves. With the fast development of electroencephalography (EEG) sensors in current headsets and some devices, brainwave-based emotion detection becomes feasible. The IoMT devices are used to capture the brainwaves of a patient in a scenario of smart home. Also, our system involves the analysis of touch behavior as the second layer to enhance the brainwave-based emotion recognition. In the user study with 60 participants, the results indicate the viability and effectiveness of our approach in detecting emotions like comfortable and uncomfortable, which can complement existing emotion-aware healthcare applications and mechanisms.

O estudo verificou o efeito da estimulação cerebral associada aos exercícios com alta demanda cognitiva em variáveis cognitivas e atividade cerebral de idosos com CCL. 18 idosos de ambos os gêneros, idade igual ou superior a 60 anos, diagnosticados com CCL foram divididos em Grupo Controle (GC, n=8); Grupo Experimental (GE, n=10). Foram avaliados o tempo de reação, memória de trabalho e potência relativa das ondas cerebrais. O GC permaneceu frequentando reuniões de treinamento da memória. O GE teve estimuladas as ondas cerebrais Alfa associado a atividades motoras que exigiam alta demanda cognitiva. Foram feitas sessões semanais de 15 min de estimulação cerebral + 25 min de atividades motoras durante 7 semanas. Após esse período os indivíduos tiveram novamente as variáveis avaliadas. Teste T foi utilizado nas comparações e verificou-se de forma descritiva que o GE obteve um desempenho melhor que o grupo controle nos testes. Na potência das ondas cerebrais Teta e Delta o GC registrou aumento Teta nas áreas parietais, e Delta tanto nas áreas parietais quanto frontais. O GE teve diminuição da potência da onda Teta nas áreas parietais e frontais. A potência da onda Alfa diminuiu em ambos os grupos, com menor redução no GE. Nenhuma das comparações entre os grupos se mostrou significantes estatisticamente. Conclui-se que a associação dos estímulos físicos e cognitivos aplicados em sessões semanais de 40 minutos durante 7 semanas não foi suficiente para produzir resultados estatisticamente significativos. Contudo, os resultados descritivos observados se assemelham aos de outros que apontam a eficiência de treinamentos que associem estímulos físicos e cognitivos durante períodos de intervenção mais prolongados.

OBJECTIVES This study was conducted to figure out the effects of kendo training on the activity of brainwaves by analyzing changes in brainwaves during chudan-no-kamae and three-movement-striking, which are basic movements of kendo, in elementary school students who undergo kendo training and comparing left and right side brainwave activities to find out differences.METHODS This study was conducted with 29 male elementary school students living in K City who were experts in kendo with a career as an athlete or a kendo training period not shorter than six months. The brainwave activity was measured by measuring and analyzing brainwaves at eight regions at the prefrontal lobe, the frontal lobe, the temporal lobe, and the occipital lobe using brainwave measuring equipment. Brainwaves were measured for 5 minutes in a sitting position in a stable state and were also measured for 5 minutes after 15 minutes of the chudan-no-kamae movement, which is aiming at the opponent with the point of the sword while moving along eight moving lines of the opponents, and for 5 minutes after 15 minutes of three striking movements.RESULTS Alpha waves showed higher brainwave activity during the stable state than during the kendo movements (three striking movements, chudan-no-kamae) while SMR, M-beta, and H-beta showed higher brainwave activity after kendo movements than during the resting state. The asymmetry between the left and right sides increased after the movements compared to during the resting state.CONCLUSIONS These results are considered attributable to kendo training that partly increased their concentration thereby increasing brainwave activity. However, it is unclear that kendo training will be helpful for concentration through physical activities and brain activity in this study. Therefore, further study should be conducted with the measurement of factors related to emotional aspects, concentration, and brainwaves according to kendo training.

Background: Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals. Objective: Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis. Methods: Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed. Results: Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08). Conclusion: The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.

As an emerging communication modality, brainwaves can be used to control robots for seamless assembly, especially in noisy environments where voice recognition is not reliable or when an operator is occupied with other tasks and unable to make gestures. This paper investigates human-robot collaborative assembly based on function blocks and driven by brainwaves. Using wavelet transform, brainwaves measured by EEG sensors are converted to time-frequency images and subsequently classified by a convolutional neural network (CNN) as commands to trigger a network of function blocks for assembly actions. The effectiveness of the system is experimentally validated through an engine-assembly case study.

Classification of brainwaves in recordings is of considerable interest to neuroscience and medical communities. Classification techniques used presently depend on the extraction of low-level features from the recordings, which in turn affects the classification performance. To alleviate this problem, this paper proposes an end-to-end approach using Convolutional Neural Network (CNN) which has been shown to detect complex patterns in a signal by exploiting its spatiotemporal nature. The present study uses time and frequency axes for the classification using synthesized Local Field Potential (LFP) data. The results are analyzed and compared with the FFT technique. In all the results, the CNN outperforms the FFT by a significant margin especially when the noise level is high. This study also sheds light on certain signal characteristics affecting network performance.

In this study, we sought to evaluate the efficiencies of multiple machine learning algorithms in detecting neonates’ Frequency Following Responses (FFRs). We recorded continuous brainwaves from 43 American neonates in response to a pre-recorded monosyllable/i/with a rising frequency contour. Recordings were classified into response and no response categories. Six response features were extracted from each recording and served as predictors in FFR identification. Twenty-three supervised machine learning algorithms with mean efficiency values of 86.0%, 94.4%, 97.2%, and 97.5% when 1, 10, 100, and 1000 random iterations were implemented, respectively. These high efficiency values obtained from the neonatal FFRs demonstrate that machine learning algorithms can help assess pitch processing in neonates and can be applied to auditory screening and intervention services for neonates at risk for disorders associated with decreased pitch processing.

The English as a foreign language (EFL) learners’ levels of attention and meditation as well as brainwaves while interacting with an interlocutor in three different second-language (L2) socializati...

The Internet-of-Things (IoT) is an evolutionary paradigm seamlessly integrating an enormous number of smart objects within the Internet. Recently, with the rapid growth and universality of wearable technology, novel security threats are emerging at the system level as well as at edge nodes in IoT-based networks. In this study, we envision a future IoT scenario in which end-users are with smart wearable objects related to human brainwave retrieval. A novel transparent authentication system using brainwaves as bio-features for IoT-based networks is proposed. In brief, this study first provides a comprehensive review of transparent authentication in recent years and presents the state of the art of this important research field. Second, we investigate the feasibility of extracting long-term memory ability from users’ brainwaves. Third, we conduct the bio-features identified in the brainwaves of users as authentication tokens in the proposed authentication system which transparently performs continuous (or real-time) entity verification in the background without the need for direct input from the user. Experiment results demonstrate the efficacy of the proposed authentication system in achieving high verification accuracy.

Religious buildings have a significant role in Muslims’ culture. The architects have developed some features in designing such buildings that create a unique sense of comfort and spiritual state in...

This work aims to investigate the possibility of reducing the occurrence of marine accidents by avoiding the unsuitability of marine transportation personnel. Recognizing that a majority of marine accidents arise from the improper reactions of transportation workers, we propose to evaluate the suitability of transportation personnel with the aid of brainwave analysis and Virtual Reality (VR) technology. By analyzing the brainwaves of a person facing operation situations simulated via VR, it may be possible to predict his/her responses in the situations and, thereby, determine if he/she is suitable to be involved in transportation work. Our preliminary experiments show that the brainwave analyses do have the capability of evaluating the appropriateness of the decisions made by a person when he/she faces operation situations.

Stress has become a common thing in today’s life. Because of the fast pace, it is a top health concern. Psychologists say that if they don’t learn healthy ways to manage stress now, it could have serious long-term health implications, such as depression, anxiety, high blood pressure, diabetes. Currently people are suffering from stress issues everywhere. Since last few decades stress has been detected in most of the people and there has been recorded severely increasing effect of stress on human body. Stress can alter complete health which leads to reducing the tolerating power of the human. In metro cities this condition is more severe. Hence it is necessary to identify stress before it causes any serious harm and treat on it. In this project we are presenting stress detection technique and lower it with the help of Music therapy. As there are many benefits of music so by taking advantage of it we are minimizing stress level to normal state. Detection of stress using brainwaves is a non-invasive method. We have selected 42 students who were about to go for their exam. Firstly we have recorded brainwaves before they were going to exam, then immediately after finishing their exam we have recorded brainwaves lastly they were allowed to listen to their favorite music and then recorded their brainwaves. From analysis it is clear that stressed brainwave were reduced to 29% by listening music.

The cognitive sciences have witnessed a growing interest in cognitive and neural basis of human creativity. Music improvisations constitute an ideal paradigm to study creativity, but the underlying cognitive processes remain poorly understood. In addition, studies on music improvisations using scales other than the major and minor chords are scarce. Middle Eastern Music is characterized by the additional use of microtones, resulting in a tonal–spatial system called Maqam. No EEG correlates have been proposed yet for the eight most commonly used maqams. The Ney, an end-blown flute that is popular and widely used in the Middle East was used by a professional musician to perform 24 improvisations at low, medium, and high tempos. Using the EMOTIV EPOC+, a 14-channel wireless EEG headset, brainwaves were recorded and quantified before and during improvisations. Pairwise comparisons were calculated using IBM-SPSS and a principal component analysis was used to evaluate the variability between the maqams. A significant increase of low frequency bands theta power and alpha power were observed at the frontal left and temporal left area as well as a significant increase in higher frequency bands beta-high bands and gamma at the right temporal and left parietal area. This study reveals the first EEG observations of the eight most commonly used maqam and is proposing EEG signatures for various maqams.

Neuroscience is one of the fastest growing STEM fields, yet its presence in K-12 science education is very limited, partially due to the lack of accessible teaching materials and tools. Here, we describe a new high school neuroscience curriculum (“BrainWaves”), where students utilize recent advances in low-cost, portable Electroencephalography (EEG) technology to investigate their own brain activity. This semester-long curriculum is supported by science mentors and an educational application that guides students through the process of recording and analyzing their own brain activity. Evaluation data collected in 5 public New York City schools in 2018/19 indicate significant positive shifts in content knowledge and self-efficacy among students who participated in BrainWaves compared to students in other courses of the same teacher. The quantitative findings are supported by interviews, where students reported increased appreciation for neuroscience and college readiness and discussed the benefits of collaborating with scientists and using portable brain technology in classrooms.

Within the workplace, it is essential to have prior training for the making of a product, either for the training of new staff or improvement in a process. The only disadvantage is that the learning task needs repetition several times until the worker can carry out the assigned task efficiently and within the set time. Unfortunately, in the literature review performed in the area of neuroergonomics, information has not been found regarding the workload effects within the brainwaves during the attainment of a task learning curve. Therefore, this work focused on a learning curve experiment that consists of the manufacture of a product with Legos in an assembly line involving seven people. A wireless Emotiv headband comprised of fourteen Electroencephalogram (EEG) electrodes along with the Emotiv PRO software were used for the brainwaves recording. After performing the statistical analysis, it was found that the presence of more significant brainwave changes occurred during the first ten minutes of the product manufacturing, and these changes were located in the frontal region of the cerebral cortex. The results of this study also indicated higher activity within the Low Beta, High Beta, and Gamma brainwaves than with respect to other brainwaves. This research will assist in the future implementation of more efficient processes based on repetitive tasks

GenieMo is a real-time communication tool based on LiveScan3D, created to empower volumetric content creation and communication for social good. It presents a potentially democratising set of tools that can demystify technological processes that seem out of reach when presented by corporate "big tech". This paper explains how working with GenieMo develops some of the potentials in earlier works of telematic art, using a capture system, social hot-casting with additional real-time brainwave recording and visualisations to create something new that is also rooted in the history of immersive art.

Evaluation of the degree of attention has very important practical significance and application prospects in our lives. It plays considerably important functions in the fields of education, medical treatment, and automatic driving, and assists people in assessing psychological states automatically and gives early warning. However, there are some problems in the existing research, such as simplex data modality and simple modeling, which make the algorithm difficult to deploy in real circumstances. To solve these problems, a new system of attention degree evaluation, called DeepFocus, is put forward in this article. We propose an attention evaluation method based on multi-modal data and multi-scenario modeling for the first time. In addition, the relationship between emotional data and attention is analyzed in depth, and labels are corrected with emotional data. Based on the DeepFocus system, a completely new attention enhancement system is constructed, and the algorithm is deployed in a practical application for students to perceive and enhance their attention. It can be foreseen that our algorithm could explore people's inward world and assess users' attention degree accurately and comprehensively to help people work, study, and live better with higher efficiency in the near future.

The purpose of this study is to investigate whether the feedback designed based on EEG (electroencephalography) signals contributes to an individual's creative performance in an immersive virtual reality setting. Two specific forms of feedback were used. The first one was “reminder feedback,” given when brainwaves indicated the participant's attention was not concentrated. The second one was “encouraging feedback,” given when brainwaves indicated that the participant's attention was very concentrated. Sixty high school students were randomly assigned to participate in an open-ended design challenge in an immersive virtual reality setting. Twenty (N = 20) participants received no feedback; twenty (N = 20) participants received reminder feedback; and another twenty (N = 20) participants received encouraging feedback. Findings showed that the participants who received reminder feedback had higher-quality creative products than those in a group with no feedback or encouraging feedback. In addition, EEG feedback also had an impact on the participant's attention and flow state. These findings are discussed in terms of feedback impacts, study limitations, and future research directions.

Neural activity that occur during motor movement, speech, thought, and various other events can be observed in the form of brainwaves composed of synchronized electrical pulses emitted from adjoining communicative neurons. Observations of these brainwaves have been made possible through neurodevices, which can detect changes in electrical and/or mechanical parameters. For decades, the field of neuroscience has been enriched by the utilization of neurotechnologies at the microscale, which has begun to gain further enhancement with the introduction of nanotechnology. For example, microelectrodes were initially used for only extracellular measurements, but over the past decade, developments have been made to also record intracellular signals. Likewise, nanoknives, which gained popularity due to their versatility, can now be used for both fabricating bio-Micro-Electro-Mechanical Systems (MEMS) and also as a neurosurgery tool. Thus, considerable efforts have been made over the years to make micro- and nanosystems reliable, accurate, and sensitive to neural activity. In the late 20th century, several sophisticated technologies, including magnetic resonance imaging (MRI), computed tomography (CT), and intracranial pressure (ICP) monitoring have been integrated with MEMS. Furthermore, existing biotechnologies are being miniaturized at both the system and component level. For example, there is a remarkable interest in the field of neuroscience to utilize microfluidic technology as a diagnostic tool using specimens such as cerebrospinal fluid (CSF). Microfluidic devices are also employed as biocompatible drug delivery systems to target cells, tissues, and organs. This paper summarizes the recent developments in micro- and nano-scale neurotechnologies, including devices, fabrication processes, detection methods, their implementation challenges, in neural stimulation, monitoring, and drug delivery. This review discusses recent developments in micro and nanotechnologies, fabrication methods, and their implementation in neuroimaging, neurostimulation, monitoring of neural activities, and neural drug delivery.

Human–plant interactions have positive effects on human health. However, the effects of plants and the environment on psychophysiological responses have not been thoroughly explored. This study examined the physiological (electroencephalography (EEG)) and psychological (the semantic differential method (SDM) and State-Trait Anxiety Inventory (STAI)) responses of adults viewing different landscape pictures. The physiological responses of 50 Chinese students were recorded by measuring blood pressure and EEG, and psychological responses were measured using the STAI and SDM. The following treatments were assessed: (1) urban city traffic photographs (control), T0; (2) garden landscape photographs, T1; (3) natural scenery landscape photographs, T2; (4) forest landscape photographs, T3 and (5) city landscape photographs, T4. Students’ blood pressure was lowered after visual stimulation with different landscape pictures than with the control. In the (SDM) evaluation, students felt more natural, relaxed and comfortable after viewing landscape pictures and had low anxiety scores. EEG results showed higher alpha, beta, delta, theta and gamma frequencies after visual stimulation with landscape pictures than with the control. In conclusion, different landscape pictures have psychophysiological relaxation effects on adults.

This investigation was inspired by an increasing global issue on how to improve soil quality while using alternative preparations instead of synthetic fertilizers. The main aim of a three-year study was to investigate the influence of horn-manure preparation on enzyme activity and nutrient content in soil and pumpkin yield. The results showed that significantly higher amounts of P (respectively 106 and 79 mg kg−1 CAL), K (149 and 106 mg kg−1 CAL), nitrogen (5.41 and 3.21 mg kg−1), ammoniacal nitrogen (9.38 and 3.45 mg kg−1) and mineral nitrogen (7.97 and 5.67 mg kg−1) were measured in the plots where the horn-manure preparation was used. A higher activity of the soil enzymes (urease activity was 1.93 times higher and the saccharase activity was 1.05 times higher) were identified with horn-manure. The average soil CO2 flux (Fc) value, when using horn-manure preparation (from 56 till 70 day), was significantly higher by 5.32% in the middle of the growing season. The yield of pumpkin was significantly increased by 18% with horn manure treatments. Significant positive correlations were identified between pumpkin yield and urease activity, and saccharase activity, as well as soil P and K.

Attention is the ability to facilitate processing perceptually salient information while blocking the irrelevant information to an ongoing task. For example, visual attention is a complex phenomenon of searching for a target while filtering out competing stimuli. In the present study, we developed a new Brain-Computer Interface (BCI) platform to decode brainwave patterns during sustained attention in a participant. Scalp electroencephalography (EEG) signals using a wireless headset were collected in real time during a visual attention task. In our experimental protocol, we primed participants to discriminate a sequence of composite images. Each image was a fair superimposition of a scene and a face image. The participants were asked to respond to the intended subcategory (e.g., indoor scenes) while withholding their responses for the irrelevant subcategories (e.g., outdoor scenes). We developed an individualized model using machine learning techniques to decode attentional state of the participant based on their brainwaves. Our model revealed the instantaneous attention towards face and scene categories. We conducted the experiment with six volunteer participants. The average decoding accuracy of our model was about 77%, which was comparable with a former study using functional magnetic resonance imaging (fMRI). The present work was an attempt to reveal momentary level of sustained attention using EEG signals. The platform may have potential applications in visual attention evaluation and closed-loop brainwave regulation in future.

In this study, the person verification using brainwaves evoked by unperceivable vibration stimulation is proposed. Brainwaves in the participants were measured while they were given unperceivable vibration stimuli, and their inducibility and individuality were examined on the basis of a t-test using spectral content rates in θ, α, and β wavebands. In addition, the verification performance using the content rates as an individual feature was evaluated using Euclidian distance matching. The average equal error rate of all electrodes was approximately 34%.

A driver’s mental and physical states while driving on hazardous roads significantly determine the incident of traffic accident. The objectives of this paper are to analyze the impact of road complexity on the psychophysiological load experienced by drivers through the use of Electroencephalography (EEG). Three conditions were examined through driving simulation, namely motorway, rural road, and city road. The data were collected from three respondents (drivers) who had different driving experiences, including < 3 years, 3 to 5 years, and > 5 years. Besides, each respondent would go through two tests with different situations: a normal situation and interfered situation (noises). The tool used was Emotive EPOC neuroheadset with 5 channels (electrode) which represent brain parts, such as the frontal (AF3 and AF4), temporal (T7 and T8), and parietal/occipital Pz. The simulation test results show that the beta signal for the motorway road situation in the occipital lobe, which functioned as visual, is more dominant compared to electrodes in other parts. Meanwhile, data from the rural road and the city road indicate a strong signal of emotions and visuals. In addition, based on the metrics performance result, the drivers’ level of stress reached its highest on the city road, as much as 45, followed by the rural road = 44 and the motorway = 42. While for the concentration index, the city road achieved 47, the rural road = 50 and the motorway = 53. EEG can be used as the basis for drivers performance assessment within different road situations so that the alert system for drivers can be engineered better.

To ensure the direct delivery of therapeutic hypothermia at a selected constant temperature to the injured brain, a newly innovated direct brain cooling system was constructed. The practicality, effectiveness, and safety of this system were clinically tested in our initial series of 14 patients with severe head injuries. The patients were randomized into two groups: direct brain cooling at 32°C and the control group. All of them received intracranial pressure (ICP), focal brain oxygenation, brain temperature, and direct cortical brainwave monitoring. The direct brain cooling group did better in the Extended Glasgow Outcome Scale at the time of discharge and at 6 months after trauma. This could be owing to a trend in the monitored parameters; reduction in ICP, increment in cerebral perfusion pressure, optimal brain redox regulation, near-normal brain temperature, and lessening of epileptic-like brainwave activities are likely the reasons for better outcomes in the cooling group. Finally, this study depicts interesting cortical brainwaves during a transition time from being alive to dead. It is believed that the demonstrated cortical brainwaves follow the principles of quantum physics.

This study aims to measure music tone impact on the behavior of ADHD Children in 3 kinds of behavior attention, relaxation, and concentration related to 3 kinds of brainwave 1.Theta wave - Attention 2. Alpha wave - Relaxation 3. Beta - Concentration. In the experiment, we controlled study employing 30 students with ADHD and 30 students without ADHD (total 60 students) ranging in age from 7 to 9 years in Thailand, participated in music listening sessions of comparison design in two different tones of music by the piano melody that was composed for this research in C tone (major key) and G tone (minor key) which is the tone is lower than C tone. In the experiment, all 60 students wear the macrotellect brain-link lite EEG headset V2.0 device meanwhile 3 brainwaves 1. Theta wave 2. Alpha wave 3. Beta was collected from them. The results showed that the music tone has impacted the behavior of ADHD children because the experiment found that the brain waves score of ADHD children collected from the 3 kinds of brainwaves were changed significantly.

Emotions are experienced by individuals in their daily life inevitably. Emotions are integral part of man’s actions separated in two distinct categories: positive and negative. There is a need to classify positive and negative emotion, as emotions play a vital role in a person’s life. Therefore, in this paper, emotion classification using EEG signals with a portable EEG device is proposed. Brainwaves are recorded using a 4-channel electroencephalogram (EEG) device called MUSE2. This research evaluates and compares the performance of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Genetic Programming (GP), and Hybrid Mutation based Genetic Programming (HMGP) for recognition of emotions using brainwaves. The experimental findings indicate that the proposed portable brainwaves-based emotion recognition model using the HMGP classifier provides 84.44% classification accuracy for two classes. The findings have shown better results relative to state-of-the-art approaches of the proposed system and have shown that proposed methodology will maintain greater secrecy.

Future digital lives are predicted to extend beyond mobile smart phoneswith devices appearing as standard eyeglasses having settings for Extended Reality (XR).Thiswill be so that what one really experiences and what is computer-generated will be so tightly mixed together that a person will not be able to distinguish between what is real and what is an illusion. Rather than sliding a finger across a touch screen on a smart phone, it will be possible to make things happen by moving our eyes or by brainwaves. Talking with someone or playing an online game will involve seeing that person in the same room and being able to touch and feel him/her via tactile technology. XR will be used in a variety of education situations with head mounted displays (HMDs) in classrooms for all children and also in home environments where those being educated have their own headset and system; medical students and surgeons will be educated in practical skills by using virtual humans rather thancadavers; oilrig and wind-farm workers will understand how to handle maintenance, repairs, and emergencies, before they ever leave the home office (Abridged from original call for chapters for this book). This chapter reviews texts selected for this volume on Gaming, VR, and immersive technologies for education and in training. It begins with a brief introduction text speculating impact related to well-being.

Authentication and authorization are an indispensable piece of security in computer-based frameworks. As an option for biometrics, electroencephalography (EEG) authentication (authorization) presents focal points contrasted with other biological qualities. Brainwaves are difficult to reproduce, and diverse mental undertakings produce various brainwaves. This examination researches the parts of execution and time-invariance of the EEG-based confirmation. Two arrangements of trials are done to record EEG of various people. We actualize the utilization of artificial intelligence (AI), for example, support vector machine (SVM) and deep neural network (DNN) to characterize EEG of subjects. The correlation between EEG highlights, anodes position, and a mental errand is made. We accomplish more than 90% order exactness utilizing three kinds of highlights from four electrodes. Information from prior meetings is utilized as AI preparing information and information from later meeting are grouped. We discovered that characterization precision diminishes after some time, and inactive undertakings perform in a way that is better than dynamic errands.

Participants’ feelings and impressions utilizing electroencephalography (EEG) and the effectiveness of code are compared for different types of programming sessions. EEG information is obtained as an alternate viewpoint during three programming sessions (solo, pair, and mob programming). MindWave Mobile 2 (brainwave detector) is equipped to collect the attention levels, meditation levels, and EEG brainwaves. These data are utilized to distinguish efficiencies, weaknesses, and points of interest by programming session. The results provide preliminary information to distinguish between the three sessions, but further studies are necessary to make firm conclusions. Additionally, alternative methods or systems are required to analyze the collected data.

There is ongoing discussion whether the cellular or mobile phone usage causes any health effects. The aim of this research is to investigate the effects of mobile phone usage on human brainwaves using electroencephalograph (EEG). The brainwave signals were analyzed using Statistical Package for the Social Sciences (SPSS). Thirty samples were interviewed prior to EEG recording. Then, the EEG recordings were performed for three sessions; before, during and after the phone calls. The findings show that alpha is dominant compared to the other frequency bands. In addition, value for the left brainwaves always higher than the right for all frequency bands which means that the samples were left brain dominant. The overall correlation between left and right brainwaves signal for all bands shows decrement for during and after phone calls, thus reducing brainwaves balancing. Therefore, there is evidence that the usage of mobile phones affect the brainwaves.

Affective personality traits have been associated with a risk of developing mental and cognitive disorders and can be informative for early detection and management of such disorders. However, conventional personality trait detection is often biased and unreliable, as it depends on the honesty of the subjects when filling out the lengthy questionnaires. In this paper, we propose a method for objective detection of personality traits using physiological signals. Subjects are shown affective images and videos to evoke a range of emotions. The electrical activity of the brain is captured using EEG during this process and the multi-channel EEG data is processed to compute the inter-hemispheric asynchrony of the brainwaves. The most discriminative features are selected and then used to build a machine learning classifier, which is trained to predict 16 personality traits. Our results show high predictive accuracy for both image and video stimuli individually, and an improvement when the two stimuli are combined, achieving a 95.49% accuracy. Most of the selected discriminative features were found to be extracted from the alpha frequency band. Our work shows that personality traits can be accurately detected with EEG data, suggesting possible use in practical applications for early detection of mental and cognitive disorders.

The fast pace of today’s life has undoubtedly brought ease in life but carried stress in every walk of life. Stress is a state of mind which leads to emotional instability leading to number of health issues. This instability needs to be addressed technically as the existing systems do not measure the stress of everyone with any advanced system rather counselling is one of the areas to conclude about stress. Existing technical systems like electroencephalography (EEG) are used to record the brainwaves of a patient who does have typical symptoms of abnormality in health. The project discussed in this paper is used to measure & record the brain waves of a person. These recorded brainwaves further are analyzed to define the state of mind of that person. This analysis is of great help in maintaining the physical and mental health of the person before it creates a negative impact. Further this system can be used to reduce the stress level of a person and making the person feel relaxed.

There are many factors responsible for improper sleep quality and sleep quantity in day-to-day life manner. Out of these factors, some are more important impact on human for the disorder of sleep patterns that is psychological, behavioral, physiological, social, cultural, and environmental. Therefore, sleep quantification is also one basic requirement with subject to analyze the brainwaves of subject during sleep time and investigate the disorder patterns. Most of the sleep experts were hypothesized that there could be a certain relationship between sleep disturbances and its consequence diseases. In this chapter, we are more focused about analysis of the brain patterns of different stages of sleep of a subject during sleep and its behaviors. This chapter introduces the background of sleep, electrophysiological parameters of sleep, individual sleep stages behavior, different types of sleep-related disorders, and its treatment solutions.

At the junction between social emotional learning (SEL) and neuroscience research, there is a dynamic interplay where electroencephalography (EEG) Brain Computer Interface (BCI) technology can allow users to gain an understanding of their mental states. Students are often told to concentrate and pay attention, but they are not actively shown what is internally occurring when they lose focus. We present a curriculum that addresses this issue and that could be important for students' self-awareness. Self-awareness, a subcategory of SEL, can be used as a tool to unpack neuroscience topics for middle schoolers, who often deal with short attention spans. The objective of our design is to help students understand and reflect on how concentration affects brainwaves and how it can strengthen their neural connections. By enabling them to experience and visualize this mental state, middle schoolers can become self-aware of how their cognitive functions change throughout this critical stage of development.

This paper reviews the evidence on why people watch so much TV/video – up over 15% in the last 25 years among US adults, including online viewing. Previous media research suggests that two important reasons are that viewing helps people (i) relax and (ii) escape from their worries. Lab-based EEG and reaction time studies confirm this and explain it, finding that watching TV/video (i) generates brainwaves associated with pleasant, wakeful relaxation and (ii) automatically absorbs cognitive capacity, taking viewers’ minds off other things. These findings need further testing and refinement for today’s increasingly complex TV/video environment, but the initial evidence is that the new types of viewing meet broadly similar needs to those met by traditional TV viewing and are likely cognitively processed in the same way.

This paper analyzes the problem about the growing concern regarding the effects of mobile phone radiofrequency (RF) radiation on human brainwave signals using complex techniques to capture these effects such as interviews and analysis of electroencephalography (EEG). While interviewing among 200 participants provide subjective assessments for the health conditions, EEG gives a noninvasive way of measuring the brainwave activity from sensors placed on the scalp of the human head. So, the aim of this paper is to study the effects of RF exposure from mobile phone on human brainwaves, which can provide information about the mental state of the individual.

We investigate the regulation of human brain arousal in the central nervous system and its synchronization with the autonomic nervous system affecting the facial dynamics and its behavioral gestalt. A major focus is made on the sensing observable during natural human eye to eye communication. Although the inner state of the autopoietic system is deterministic, its outer facial behavioral component nondeterministic. Beside the introduction of general validity of the classical empirical interpretation of the vigilance continuum during open eyes, we show that the facial behavior can be used as suitable surrogate measurement for specific states of mind. As a consequence we predict brainwaves from face videos formulated as inverse problem of the underlying stochastic process. Finally, we discuss the impact and range of application field.

Brain-Computer Interfaces (BCIs) facilitate communication between brains and computers. As these devices become increasingly popular outside of the medical context, research interest in brain privacy risks and countermeasures has bloomed. Several neuroprivacy threats have been identified in the literature, including brain malware, personal data being contained in collected brainwaves and the inadequacy of legal regimes with regards to neural data protection. Dozens of controls have been proposed or implemented for protecting neuroprivacy, although it has not been immediately apparent what the landscape of neuroprivacy controls consists of. This paper inventories the implemented and proposed neuroprivacy risk mitigation techniques from open source repositories, BCI providers and the academic literature. These controls are mapped to the Hoepman privacy strategies and their implementation status is described. Several research directions for ensuring the protection of neuroprivacy are identified.

This study aims to create a neurofeedback system and observe whether wearing this system assists participants in consciously changing their state of mind. Brainwaves are produced by synchronized electrical pulses from masses of neurons communicating with each other. Through the use of a Neurosky Electroencephalogram (EEG) biosensor, we can detect the brainwaves that result from neuron activity. Attention and meditation levels are two significant examples of brain activity that are reflected by the human body. Our neurofeedback system can monitor attention and meditation levels that humans are normally unable to sense. The Arduino-medium-developed program records brainwaves as attention and meditation values (eSense meter value). In addition, we constructed a vibration system that provided realtime notification to notify participants when their state of relaxation or concentration changed. With appropriate training, users of the neurofeedback system can develop strategies to control their state of mind successfully. [4]

Mind reading is often characterized as a mechanism of surveillance that ferrets out information against our will; but it can also be collaborative, involving partnerships and networks. Indeed, researchers and artists have been using human electroencephalography (EEG), to produce aggregate data sets for the creation of musical symphonies, city planning, and affective mapping. In this chapter, Littlefield examines two such projects: the “Brainwave Symphony” (2015), a promotional project associated with the Netflix series Sense8, and the work of Multimer, a company whose slogan is “Better Decisions Based on Human Signals.” She argues that both projects illustrate beliefs about the power of aggregating brain-based data for political and artistic purposes, even as each relies on constructions of brainwaves as material objects.

Recent advances in computer hardware and signal processing have made possible the use of electroencephalogram (EEG) for communication between human brain and computers and this technology is known as brain-computer interface (BCI). Locked-in patients have now a way to communicate with the outside world using BCI technology. Nowadays, BCIs are getting popularity among the researchers to control devices using brainwaves especially in providing good assistance to disabled people. Impressive development and integration of both hardware and software in BCI have been carried out in the last two decades. However, some open challenges and limitations have also been exposed in the previous researches. In this paper, we have tried to mention some critical issues of EEG based BCI system including EEG modalities, EEG acquisition, signal processing algorithm and performance evaluation. These issues need to be solved to develop error-free BCI system. In addition, possible solutions and future directions have also been discussed.

This article reports the study on the brainwaves patterns between the activity of reading the Arabic and reading Malay text among the final year undergraduates in the university of Malaysia Sarawak. Three students volunteered for the research and their brain waves were observed and recorded to find out the difference of brainwave pattern while reading Arabic and Malay language. The observation showed a dominant production of delta followed by theta while reading Arabic and Malay language. The text used for reading were from Verse 36 (Arabic and the translated version in Malay ) of the Quran. The average mean of delta wave were higher for the reading of Arabic language at the frontal lobe than while reading the Malay translation. The frequency of delta rhythm of the Arabic language differed from the Malay language due to the nature of the Arabic language. The neural circuits of the rhythm from the Arabic recitation implicates not on just the spatial visual area at the parietal lobe but also the visual eye movement at the frontal region guided by the Visual system at the Medial temporal area. Keywords: EEG, Quran, Brain waves, QEEG.

We developed a game-based training system to analyse and improve the reading ability of children with Attention Deficit/Hyperactivity Disorder (ADHD). A fairy tale-based interactive narrative is used as an intervention strategy in the behaviour training system, which collects brainwaves and motion-sensing data during treatment. The system includes fairy tales as well as attention and behaviour-related tasks coupled with a brain-computer interface (BCI) and motion-sensing technology. The treatment for the children (N = 5) diagnosed with ADHD was performed for five weeks on a weekly basis, comprised of one 20-minute long adaptation session and four 40-minute long sessions. For the quantitative analysis of the treatment, pre- and post- KNISE-BAAT and general reading questionnaires were administered. Sensing data was also recorded. In-depth post-interviews were conducted after the completion of the treatment programme for qualitative analysis. The paired-samples t-test on both reading comprehension te...