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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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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]

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.

Various studies have reported the correlation between brainwaves electroencephalogram (EEG) and various sports. However, until now, the correlation between EEG signals and the Taekwondo (TKD) have not been reported. The aim of this study is to investigate the correlation of brainwaves of TKD athletes in training experiences and competition performance. The EEG signals were collected from 12 elite male TKD athletes. The β-wave was significantly increased when the heart rate of participant was >120 min−1 compared to the resting state. The γ-wave was significantly decreased during foot kicking modalities. In addition, the γ-wave was significantly increased while watching competition films when compared to that in resting state. In the simulation game, before the first and second round, the δ-wave of the winner group was significantly higher, whereas the α-and γ-waves were significantly lower than those of the loser group. While before the third round, the θ-wave of the winner group was significantly ...

Electroencephalography (EEG) is an important tool for characterizing the functioning of the brain. Studies based on EEG involve the extraction of different spectra from EEG signals. Traditional methods of extracting these brainwaves (commonly δ, θ, α, β, γ) from EEG signals, like impulse-response filtering or wavelet decomposition, are computationally inefficient or unsuitable for real-time implementation. The Maximum-Mean-Minimum (MaMeMi) filter is a signal processing algorithm that is computationally efficient for signal filtering. The response of the MaMeMi filter is dependent on pre-decided filter coefficients. An obstacle to its implementation is that the filter coefficients have to be tuned to the sampling frequency. We propose the Modified-MaMeMi (MoMaMeMi) filter, in which the choice of coefficients is independent from the sampling frequency. Furthermore, we develop a band-pass MoMaMeMi filter which is duplicated in a filter bank, to decompose EEG signals into five common brainwaves. We validate the efficiency of the proposed filter bank by the increase in Signal-to-Noise Ratio (SNR). The maximum average increase in SNR is 19.68 dB. To prove utility of the filter-bank, we statistically compare the values of windowed average power extracted from the MoMaMeMi-filtered signals, between seizure and non-seizure components of the EEG data-set. A significant difference between the distributions suggests utility for classification problems. Since EEG-signal processing algorithms are highly customised and not limited to the 5 common brainwaves reported in this paper, we also develop a program to determine filter parameters for extraction of unique frequency bands in a bespoke MoMaMeMi filter.

This study was performed on sidewalks that there can secure movement of pedestrians. The basic theme of the research is how pedestrians are influenced by the sudden hearings of horns in the walking moments. In this study on different horns chord samples were randomly presented during simultaneous Electroencephalogram (EEG) recordings to a total of 48 healthy participants aged between 25–65 years; the elicited brain responses were examined. EEG recording with visual stimuli is performed on 48 subjects at delta, theta, alpha, beta and gamma frequency bands by using 5 different horn sounds which are selected from the real traffic environments. Especially trucks with a high-end horn sound, show very significant differences in the brainwaves of the pedestrians. Therefore, it is very important for pedestrians to make legal arrangements related to the voice of the high horn, especially in the developed countries.

Reformed epistemology, roughly, is the thesis that religious belief can be rational without argument. After providing some background, I present Plantinga's defense of reformed epistemology and its influence on religious debunking arguments. I then discuss three objections to Plantinga's arguments that arise from the following topics: skeptical theism, cognitive science of religion, and basicality. I then show how reformed epistemology has recently been undergirded by a number of epistemological theories, including phenomenal conservatism and virtue epistemology. I end by noting that a good objection to reformed epistemology must criticize either a substantive epistemological theory or the application of that theory to religious belief; I also show that the famous Great Pumpkin Objection is an example of the former.

As brain dynamics fluctuate considerably across different subjects, it is challenging to design effective handcrafted features based on prior knowledge. Regarding this gap, this paper proposes a Graph-based Convolutional Recurrent Attention Model (G-CRAM) to explore EEG features across different subjects for movement intention recognition. A graph structure is first developed to embed the positioning information of EEG nodes, and then a convolutional recurrent attention model learns EEG features from both spatial and temporal dimensions and adaptively emphasizes on the most distinguishable temporal periods. The proposed approach is validated on two public movement intention EEG datasets. The results show that the GCRAM achieves superior performance to state-of-the-art methods regarding recognition accuracy and ROC-AUC. Furthermore, model interpreting studies reveal the learning process of different neural network components and demonstrate that the proposed model can extract detailed features efficiently.

Monaural beats in this studied was generated by the principles of binaural beats which both of principle is same but monaural beats is not via headphone. Nevertheless, many previous researches used the binaural beats to create a new frequency of via headphone only. Which that might be make the listener or patient feel uncomfortable. So, for using a non-contact, in this research will be presented a method that was developed for induce brain waves on human based on monaural beats that were transmitted through the speakers which was improved by theory of low frequency. Principle of music and audio engineering were used for designed a laboratory test size 2.60 x 4.03 x 3 meter together with the frequency of 131.81 Hz and 121.81 Hz that were generated and transmitted via audio speakers. Then we will detect the frequency with specific microphones and software. For measure up the brain waves, 25 subjects will be wearing EEG (Electroencephalography) headset and collect data to analysis in MATLAB base on biomedical engineering technique. From the result we found that the monaural beats can be sent to the subjects. In addition, it also modulation the brain signal of subjects.

We propose the use of SSVEP (Steady State Visual Evoked Potentials) in such a way as to cause the eye itself to function as a camera. By recording brainwaves in response to flickering visual stimuli, we read the “mind’s eye” and are able to successfully reconstruct a visual image of subject matter that a person is looking at. In addition to causing the eye itself to function as a camera (see Fig 1), we also propose a new way to visualize and photograph human vision and human perception, i.e. a new way to see and understand human vision. This new “meta-vision” (vision of vision) has many applications in healthcare, from testing human vision to furthering our understanding of the brain.

In recent years, many studies have shown that music affects brainwaves. However, music also has the power to stimulate strong emotions. If music is classified by emotion, will the music of different emotions have different effects on brain wavesƒ The focus of this study was to observe brainwaves as subjects listen to different emotional types of music to verify whether the music of different emotional categories has different effects on brain waves. We classify music by the circumplex model of emotion and select four pieces of music from the different subdivision in that model as our experimental music. Study adopt MindWave Mobile as the primary sensing component, which is a sensor for detecting brainwave and output the electroencephalogram (EEG). The collected brainwave data was tested and processed by the experimental module of this study and use T-Test to evaluate the effects of music on brainwaves. In the experimental results, we found that the arousal score of music in the circumplex model of emotion does have the positive relationship with the alpha wave, and for other brainwaves do not have adequate evidence to prove their connection.

For instant recognition of visual attentiveness, we established a set of studies based on signal conversion and machine learning of electroencephalogram (EEG). In this work, we invited twelve participants who were asked to play testing games for ensuing paying visual attention or to take a rest for a relaxed state. The brainwaves of participants were recorded by an EEG monitor during the experiments. EEG signals were transferred from time-domain into frequency-domain signals by fast Fourier transform (FFT) to obtain the frequency distributions of brainwaves of different visual attention states. The frequency information was then inputted into a probabilistic neural network (PNN) to build a discrimination model and to learn the rules that could determine an EEG epoch belongs to paying attention or not. As a type of supervised feedforward neural networks, PNN benefits high training speed and good error tolerance which is suitable for instant classification tasks. Given a set of training samples, PNN can train the predictable model of the specific EEG features by supervised learning algorithm, performing a classifier for visual attentiveness. In this paper, the proposed method successfully offers efficient differentiation for the assessment of visual attentiveness using FFT and PNN. The predictive model can distinguish the EEG epoch with attentive or relaxed states, which has an average accuracy higher than 82% for twelve participants. This attention classifier is expected to aid smart lighting control, specifically in assessing how different lighting situations will influence users’ visual work concentration.

Electroencephalography (EEG) has been a recent subject for various medical, clinical and non-clinical uses and applications. This is due to the extensive amount of information hidden within the human EEG brainwaves and brain neural activities. Within this article, we shall present another novel approach related to mining with well, healthy, and clinically approved EEG brainwaves related to events related to human vision EEG, and how a robotics system can be controlled and moved through such Brain-Computer Interface (BCI). The adopted thought is more related to the usage of time related features, and the time-spectral (wavelet) features extraction, hence to recognize the complicated eye-thoughts EEG patterns through a recognition algorithm. Two approaches have been used for the pattern recognition, this include the SVM, and the PAC based Random Forest with 30 trees. Results also include how a robotics system can be controlled using the EEG brainwaves.

The purpose of this study was to study the use of Neurosky Smarter Kit in brain exercise training program on both attention and meditation levels’ changes in elderly. The sample of this study were nine participants, which were four men and ten women, aged 61-84 years old. All participants were measured their attention and meditation levels by Neurosky Smarter Kit prior to the brain training program. During the training, should any participant could not proceed to the next session, he/she was measured their attention and meditation levels again. They were allowed to break for fifteen to thirty minutes or as much as they needed. Then, the second brain training was done and that participants were measured their attention and meditation levels again. The result showed that attention and meditation levels were gradually increased with no statistically significant. Both theta and alpha brainwaves were increased statistically significant.

The objective of this study was the study of brainwave activity and cognitive performance by meditation yoga. Study design was experimental design. This study compared the brainwaves of healthy people both before and after practicing by meditation yoga. Samples consisted of ten healthy people with no history of illness, no brain disease, never attended brain surgery. By conducting electroencephalogram (EEG) measurement by the NeuroSky Smarter Kit through the program “MindWave Mobile” showed average level of meditation and attention. Participants also practiced 21 meditation postures yoga program. The results revealed that the post-test scores of all ten healthy people that practiced with 21 meditation postures yoga program had meditation and attention score higher than before training with significant difference at 95% confidence level.

Active BCI programs may play a pivotal role in aiding the expression and communication for those who are unable to. According to the world health organization, 250–500,000 people suffer a serious spinal cord injury or debilitating nerve damage every year. Many of these cases lead the partial to full paralysis and/or blindness. It has been shown that EEG technology can aid patients’ communication abilities. The current programs on the market rely on visual cues to elicit a neural response and require constant eye movements, that may not be possible for some patients. In this work, an active BCI painting prototype was presented that did not rely on visual cues to elicit a neuro response. Based on the premise that the EEG signals can be used by a system more directly to interpret the commands of the user, a continuous painting BCI acted like a brush on a virtual canvas. Assigning the frequency bands alpha, beta, and delta to red, blue, and green, respectively; a volunteer performed a small painting demonstration. Thus deeming the prototype a good basis or starting point for a low effort active BCI painting application.

It is amazing to know that cells in our brain are electrical signals which are generated by information processing units of the brain called Neurons. The most important factor for mapping these electrical activities to develop robust Brain-Computer Interfaces is the methods used for processing these electrical signals. The electrical activities in the brain can be captured using multiple electrophysical measures such as Electroencephalogram (EEG). EEG captures neurocognitive processes, the cognitive dynamics in the time frame in which cognition occurs, and multidimensional space of brain processing. This comprehensive survey presents various processing techniques and algorithms for developing robust Brain-Computer Interfaces using EEG.

Differences in baseline electroencephalogram (EEG) activity have been found among long-time practitioners of meditation (3+ years) in comparison to novice meditators ( KEYWORDS: EEG, electroencephalogram, brain, yoga, mindfulness, alpha, beta, meditation, OHM

Rapid advances in technology have helped to improve human life, specifically for people with disabilities. These people cannot control their muscles, write, or talk properly because of disruptions in their nervous system. However, many of them can control their brain activity and eye movement. Electroencephalography (EEG) tools are used to track brainwaves and eyes blink activities and convert them to useful metrics. Using EEG tools, people with movement or speech disabilities can only be trained to think of any action they want to take to make it real. This paper proposes a system that can be integrated into a wheelchair to control the movements using brainwaves signals. In this paper, the NeuroSky mind-wave mobile headset is used as an EEG tool. NeuroSky signals combined with neural network MATLAB code as training code with the adjusted parameters of this code are supplied to the standalone Arduino controller used to control the mini wheelchair prototype.

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2&ndash, 6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

In the beginning of this recent years, the number of children with Autism Spectrum Disorders (ASD) in Indonesia has increased. Previous studies showed that brain activity of children with ASD was found to be prominently different in frontal and temporal lobes. One of the known methods to measure brain activity in both normal and disorder brain condition is the measurement of brainwaves using electroencephalography (EEG). Therefore, this study aimed to analyze the brainwaves of frontal and temporal lobes of children with ASD, using a calculation of spectral entropy. The subjects group on this research were the children with ASD and the normal children as a control group. EEG recordings were conducted while the children from both of the group was resting and eyes closed. As a preprocessing method, data filtering and algorithm normalization were done to get EEG data. Periodogram Welch method, based on Fast Fourier Transform (FFT), was then used to calculate the power spectral entropy (PSE) of alpha and beta frequency bands. The results were show that at 14 second of children with ASD had the PSE peak. The peak that was observed in the The frontal lobe (F8) located in the right hemisphere was higher than the peak shown in the temporal lobe (T8). However, the peak of left hemisphere T7 was higher than F7.On the control object, T8 had a peak with values that was greater than the peak of F8 located in the right hemisphere. Meanwhile, for the left hemisphere, the peak of F7 was greater than T7. Based on these results, it could be concluded that the ASD object and the control object had different frontal and temporal lobe PSE conditions.

This research is aimed at analysing EEG data streams from FP1 and F7 channels that reflect activities of the human brain with respect to age and gender. It is mainly focused on finding significant variations in brainwaves detected at frontal parietal lobes of children and adults in both genders. EEG data gathering, data extraction, data cleaning and data filtering technologies were used to observe variations in brainwave patterns among children and adults especially in regions of the brain associated with logical thinking and linguistics. We used cross correlation functionality to obtain similarity between subject datasets in the sample space. Results indicate increased active brain signal patterns near the frontal parietal lobe of young male subjects than in female subjects.

This literature survey attempts to clarify different approaches considered to study the impact of the musical stimulus on the human brain using EEG Modality. Glancing at the field through various aspects of such studies specifically an experimental protocol, the EEG machine, number of channels investigated, feature extracted, categories of emotions, the brain area, the brainwaves, statistical tests, machine learning algorithms used for classification and validation of the developed model. This article comments on how these different approaches have particular weaknesses and strengths. Ultimately, this review concludes a suitable method to study the impact of the musical stimulus on brain and implications of such kind of studies.

This paper introduces a newly developed visually stimulating iOS-based application MCF for investigating colour’s impact on the brain signals for Brain-Computer Interface development. The application’s main purpose is to streamline the workflow associated with testing different stimuli colour combinations in eliciting brainwaves. Electroencephalograph headset called Emotiv EPOC was used to record the brain signals. Steady State Visual Evoked Potential paradigm was used to elicit the brainwaves. The paper briefly examines the colour science used, explains the development approach as well as presents pilot testing results confirming usability and effectiveness of the application.

This paper presents the implementation of a P300 alphabet speller study case. The speller is based on methodology in which alphabet symbols are displayed in real-time on screen to test subjects, with real-time electroencephalograph measurement of human brainwaves, with P300 event evoked potential signal detection. The implementation is done in MATLAB with Emotiv Epoc EEG+ headset.

The study focuses on the effect of Horizontal Rotation (HR) intervention on the brainwaves and sleeping quality. HR system is a custom-engineered rotating bed, designed to relax the physical body and to synchronize the electrical activity of the brain. It is known to enhance sleeping quality which resulted in a more relax and focus condition leading to healthier lifestyle. Electroencephalogram (EEG) is used to measure the brainwave electrical activity after being picked up by the metal electrodes connected at the scalp surface of a human. The brainwave signal is recorded before and after the intervention of HR undergoing for three sessions. The result shows that most participants experienced an improvement in sleeping pattern and sleeping quality. In addition, participants achieved higher Beta and Alpha brainwaves showing better focus and relaxation.

This paper presents a modest, low cost method todesign a modular electroencephalography (EEG) signal acquisitionsystem for Brain Computer Interface (BCI) application.The system is based on active electrode EEG sensors along withArduino-Uno hardware, to acquire 3 channels for recording EEGsignals. In addition to that, a graphical user interface (GUI)was developed using Matrix Laboratory (MATLAB) for the purpose of signal processing and visualization. The performanceof designed EEG acquisition system was evaluated on the basisof classification of Motor Imagery (MI) brainwaves for imaginedleft-hand (LH) and right-hand (RH) movement. The designedsystem was used to record the MI brainwaves of 10 differentsubjects and the recorded data was split into a training set andtest set. Independent Component Analysis (ICA) was used for theremoval of EEG artifacts. The feature extraction was done usingWavelet Decomposition (WD) using Morlet as mother wavelet.The wavelet decomposed signal features like Maximum Power(MP) and Approximate Frequency (AF) was used as classificationfeatures for the classification of MI brainwaves. The classificationof MI brainwave signals was done using Linear DiscriminantAnalysis (LDA) which showed the accuracy of 81.6 percentagesfor testingdata set. Thus, the designed three channel active electrode EEGdevice could be used in various BCI applications.