Brain Wave Data Research Articles

The Research and Design of Intelligent Safe-driving Detecting System Based on the Eeg Sensor

The intelligent safe-driving warning system can detects the brain wave signal of the driver through the eeg sensor and analyze the current mental state of the driver. It can process the the eeg data of the different types. It can reduce the noise of the collected brain wave data resulted in the outside factors. The small packet data can be received and analyzed by the Bluetooth receiver. The large packet data can be dealt with and analyzed by the eSense algorithm. The data of concentration and distraction can be extracted from the brain wave signal data. And then, judging the degree of the concentration and distraction, it can roughly determine the brain activity. The eeg wave signals can communicate via Bluetooth and be sent to the client and processed. At the same time, according to the set benchmark of the client, it can analyze and judge the driver’s current mental state and decide whether to remind the driver of the driving safety finally.

Authentication with What You See and Remember in the Internet of Things

The Internet-of-Things (IoT) is an emerging paradigm seamlessly integrating a great number of smart objects ubiquitously connected to the Internet. With the rise in interest in the IoT, industry and academia have introduced a variety of authentication technologies to deal with security challenges. Authentication in IoT involves not only shifting intelligent access control down to the end smart objects, but also user identification and verification. In this paper, we build an authentication system based on brainwave reactions to a chain of events. Brainwaves, as external signals of a functioning brain, provide a glimpse into how we think and react. However, seen another way, we could reasonably expect that a given action or event could be linked back to its corresponding brainwave reaction. Recently, commercial products in the form of wearable brainwave headsets have appeared on the market, opening up the possibility of exploiting brainwaves for various purposes and making this more feasible. In the proposed system, we use a commercially available brainwave headset to collect brainwave data from participants for use in the proposed authentication system. After the brainwave data collection process, we apply a machine learning-based approach to extract features from brainwaves to serve as authentication tokens in the system and support the authentication system itself.

Evoke of The Motoric Brain Waves of Driver in Car Simulator at Night by Blue Light Exposure and Visual Distraction

Driving at night need high levels of concentration for respond any visual stimulation by motoric coordination. Such concentration while driving can be mounted using blue light inside the car’s compartment, since it can inhibit the secretion of melatonin hormone, to increase drivers’ alertnessand prevent drowsiness at night. This study was aimed to evaluate the effect of blue light exposure on µ brain waves while driving at night in the simulator as a response to visual distraction. Brain waves data were recorded using electroencephalograph in brain motor areas from 7 men with predetermined criteria. Data were taken at night (7-9 pm) for 330 seconds in dark condition and blue light exposure, followed by the appearance of visual distraction automatically at 120 and 270 seconds and should be responded by braking. In blue light condition, µ brain waves were fluctuated in both motor areas since the first appearance of blue light exposure and was significantly different when compared to dark condition (p<0.05). It is concluded that blue light exposure at night in a driving simulator may increase drivers’ brain activity in the motor areas when responding to visual distraction, especially in the first appearance.

Blue Light Exposure and Visual Distraction Effect of The Cognition Brain Waves of Driver in Car Simulator at Night

Driving is a complex activity and driver must respond quickly and accurately to any visual distraction. Accidents are high while driving at night, may due to drivers’ drowsiness which is caused by the environmental light that affect the circadian rhythm. Blue light is a component of the solar spectrum and inhibit the secretion of melatonin hormone that can prevent drowsiness at night. This study is aimed to evaluate the effect of blue light exposure on β brain waves while driving at night in the simulator as a response to visual distraction. Brain waves data were obtained from electroencephalograph in brain cognition area from 7 men with predetermined criteria. Data were taken from 7–9 pm for 330 seconds in dark condition and blue light exposure followed by the appearance of visual distraction automatically at 120 and 270 seconds and should be responded by braking. The result showed than β brain waves increased under blue light which was associated with area of cognition and was significantly different when compared to dark condition (p<0.05). It is concluded that blue light exposure at night driving in simulator increase drivers’ alertness and attention when responding to visual distraction, especially in the first appearance.

Application of Electroencephalogram Physiological Experiment in Interface Design Teaching: A Case Study of Visual Cognitive Errors

Technology changes accelerated the speed of subject interaction more and more frequency in now days. Take advantage of electroencephalogram (EEG) physiological experiments to analysis the user interface (UI) of software, bring the assessment criteria of quantitative analysis to the design discipline, these methods were accepted by more and more researcher in a university or institute. As a researcher in a university, we show EEG in this paper our experiences and results in teaching students whose major in interface design with five stages, our focus is one of EEG solutions, which offers several UI possibilities. Take EEG physiological experiments to analysis the visualization cognitive errors (VCE) could help designers understand the cognition process of digital interface visual information (DIVI) by different users and improve the overall efficiency of digital interface design. Based on the composition of DIVI, VCE of DIVI were analyzed from color and layout difference. According to the experimental factors of the error trap, this paper designed two EEG physiological experiments to study the behavioral responses and EEG responses of the brain to DIVI. In order to improve the versatility of experimental conclusions in digital interface design, the EEG physiological experiments in this paper was designed under the Oddball experimental paradigm. And the experimental materials include the digital interface of PC terminal and mobile terminal. The statistical analysis results of the experimental data show that the behavioral data and brainwave data of the EEG experiment can be used as the basis for judging the cognitive errors of the digital interface visual information. However, EEG components and EEG topographic maps related to VCE of DIVI may vary significantly due to differences in experimental materials. The experiment also indicates that there are many branches in the EEG experiments to analysis the VCE of DIVI. It is helpful to improve the reliability of digital interface design by perfecting the EEG physiological experiments of these branches. We give an example of the practical problems and adequate sequences for teaching EEG approaches based on our study and experiences.

Electroencephalogram-Based Brain-Computer Interface and Lower-Limb Prosthesis Control: A Case Study.

The purpose of this study was to establish the feasibility of manipulating a prosthetic knee directly by using a brain-computer interface (BCI) system in a transfemoral amputee. Although the other forms of control could be more reliable and quick (e.g., electromyography control), the electroencephalography (EEG)-based BCI may provide amputees an alternative way to control a prosthesis directly from brain. A transfemoral amputee subject was trained to activate a knee-unlocking switch through motor imagery of the movement of his lower extremity. Surface scalp electrodes transmitted brain wave data to a software program that was keyed to activate the switch when the event-related desynchronization in EEG reached a certain threshold. After achieving more than 90% reliability for switch activation by EEG rhythm-feedback training, the subject then progressed to activating the knee-unlocking switch on a prosthesis that turned on a motor and unlocked a prosthetic knee. The project took place in the prosthetic department of a Veterans Administration medical center. The subject walked back and forth in the parallel bars and unlocked the knee for swing phase and for sitting down. The success of knee unlocking through this system was measured. Additionally, the subject filled out a questionnaire on his experiences. The success of unlocking the prosthetic knee mechanism ranged from 50 to 100% in eight test segments. The performance of the subject supports the feasibility for BCI control of a lower extremity prosthesis using surface scalp EEG electrodes. Investigating direct brain control in different types of patients is important to promote real-world BCI applications.

The Displaced ‘Dispositif’

“ Dispositif ” is a term used in film studies since the 1970s to describe the entire system of mechanical and human factors which together bring about the cinema experience. It therefore refers to (amongst other things) the space of the auditorium, the screen, the projection technology and the physiology of the spectator. Many of its qualifying components are masked from the view of participants in the system. The dispositif’s purpose is to set up the conditions for a specific type of cognitive experience, one which mirrors and extends (and in some readings, controls) the experience of its participants. The Displaced Dispositif is a performance designed for the space of a cinema theatre, but featuring the projection of fragments of early silent cinema on a coeval (1910s) film projector from the auditorium. The film fragments are live-scored by the sound artist, Shaun Lewin, using a combination of closely mic’d sources on the projector itself, luminance data from the projected image and EEG brainwave data recorded from participants during previous projections of the film. Displacing elements in the dispositif in this way, by shifting modalities, situating in parallel, feeding back and layering, draws attention to its hidden existence and creates the potential for a more knowing and informed participation in the cinema experience. It also serves to demonstrate the degree to which dispositifs of modern cinema spectatorship, which have morphed and proliferated since the widespread digitization of film heritage, have radically altered both the technological and experiential qualities of the medium. By integrating EEG data, the performance adds the dimension of electrophysiological experience to the long tradition within experimental cinema of artists calling attention to Cinema’s hidden structures. As well as challenging the dominance of the worldview propagated by the film industry, the performance also signals a means of re-engaging with the creative potential of the system itself, once unshackled from its bonds to the reality effect and freed from the limits imposed by its commercializing instincts.

Collaborative Game-Based Learning with Motion-Sensing Technology

The purpose of this study was to compare the influence of a single-player game with that of a collaborative game on students' motivation, attention levels, and relaxation levels in a motion-sensing learning environment. Participants were 20 college students in a higher education institution in southern Taiwan. A Kinect-based game with motion-sensing technology was incorporated to help the students enhance their attention levels and reduce their anxiety. Results showed that when the students worked collaboratively, they achieved higher learning motivation. In addition, brainwave data revealed that during the gaming activities, the students' attention levels in both conditions increased. It is suggested that instructors can incorporate collaborative learning environments with a Kinect motion-sensing platform to enhance students' learning motivation.

Effect of thermal sensation on emotional responses as measured through brain waves

Beyond previous research topics assessing physiological or psychological effects of an indoor environment, the present study aims to investigate the effect of thermal sensations on people's emotional responses. For achieving this purpose, chamber experiments were conducted based on three different temperatures (PMV −2, 0, +2), and results were obtained from 139 participants' brain wave data. The emotional process depending on indoor temperature was not significant; however, that depending on subjective thermal sensations were statistically significant. Since participants felt same indoor thermal condition differently, the physical indoor temperature itself had no direct significant influence to their emotions. Positivity bias was observed when participants felt neutral and slightly warm, and negativity bias was observed in all other cases. This study supports the notion that the thermal environment affects occupants' emotional responses through subjective thermal sensations.

The Research of Driver Distraction by Visual Smog on Selected Road Stretch in Slovakia

Driver distraction is one of the most common cause of traffic accidents in all countries. The previous research has shown that digital billboards can take the driver attention for more than one second. Therefore, the aim of the article is to research how can visual smog influence the driver behaviour. The road stretch in Žilina has been chosen for measuring the driver distraction by visual smog next to the road. The analysis of visual smog and traffic accidents on the selected road is included in article. Eye tracking glasses has been used to scan the gaze of driver eye. Part of the experiment was EEG device, used to record brainwave data during the testing of impact of visual smog to driver. The results regarding of driver gaze at visual smog are awful considering that the average dwell time of one billboard was more than a half second. On the other side, the average dwell time of traffic sign was only 0,2 second. In generally we can state that visual smog can influence the driver behaviour which can lead to traffic accident. For all that it is necessary to reduce the increased number of visual smog next to the road which can lead to fewer traffic accidents.

An integrated-mental brainwave system for analyses and judgments of consumer preference

With the competition expansion of consumer markets, product appearance becomes important topics when the products, which consumers make decisions to select, have similar quality and content. Hence, enterprises and companies spend a lot of time and money, and pay more attention to enhance product appearances to further attract other consumers. In comparison with using only questionnaires, obtaining consumers’ thoughts directly from their brains can accurately grasp their actual preference. Undoubtedly, it can provide effective and precious decisions for enterprises and companies. In this study, consumers’ brainwaves integrated with mental tasks are extracted through a wearable, portable, wireless electroencephalography (EEG) device. The extracted EEG data are then trained using perceptron learning to make the judgments of integrated mental works well for each subject. They are then applied to the analyses and judgments of consumer preference. Finally, questionnaires are also used as the references on the training process. They are combined with brainwaves data to create a prediction model that can significantly improve accuracy. The partial least squares are used to compare the correlation between different factors in the model and ensure the test can accurately meet consumers’ thoughts.

카페공간에 대한 수렴적 탐색상황에서의 주의집중 특성의 분석 방법에 관한 연구 - 선택적 주시데이터에 의한 뇌파 데이터 분석을 중심으로 -

카페공간에 대한 수렴적 탐색상황에서의 주의집중 특성의 분석 방법에 관한 연구 - 선택적 주시데이터에 의한 뇌파 데이터 분석을 중심으로 -

Real-time 3D fluid simulation digital art using BCI

We present a methodology about real-time 3D fluid simulation system using BCI (brain–computer interface). Spectators appreciate our work with their emotion. The advance of technology has exerted huge impacts on arts and as result a new genre called digital art has emerged. In digital art, interaction is an important element. Various types of interaction are bein g implemented in digital art using touch, hand gesture, sound, movement, etc., even brain wave. Usually, digital artists pursue interactions in their art work; the reason is that they want spectators’ deep appreciation and involvement. However, many of interactive digital art works fail to induce interaction as much as the artists expected. If visible interaction is weak, or interaction result is weak, spectators are not immersed much in the work. Nowadays, there are many interactive digital arts. In digital art, new type of interaction is needed to captivate spectators. So, we tried to implement BCI interaction digital art prototype (tentative title: Soul flow). Electroencephalography (EEG) is the recording of brain electrical activity along the scalp. We measured user’s EEG signal using MindSet (the MindSet senses EEG brainwave data to power the innovation of laboratory researchers and application developers like no other EEG device in the world. It delivers RAW signal, power frequency bands and NeuroSky eSense meters:attention, meditation. http://www.neurosky.com/products/mindset.aspx) to take user’s emotional state on appreciating digital art. We can get user’s emotional state in four categories (attention/inattention, meditation/uneasiness) and these emotional states interact with 3D fluid simulation system in real-time. In this art work prototype, each spectator experiences each different fluid (scale, speed and visual) because each spectator has different emotional state. Our art work prototype expressed the flow through water flow simulation. Flow means the mental state of operation in which a person performing an activity is fully immersed in a feeling of energized focus, full involvement, and enjoyment in the process of the activity. In essence, flow is characterized by complete absorption in what one does (The psychology of optimal experience 1990). In addition, it means stream in water flow. We pursued these concepts and expressed to 3D fluid simulation digital art using BCI. We used Unity3D and MindSet to implement our art work prototype. This study will expand the participation of the spectator in digital art. In addition, it will expand the possibilities of BCI in digital art interaction.

Designing a robot controller by using a simple brain-wave sensor and a machine learning technique

We designed a robot controller that can use unstable data, such as brain waves. The controller analyzes brain waves from a simple electroencephalograph. A user can concentrate to make the robot move faster, and relax to make it move slower. In order to judge the user's state by his brain-wave data, we adopt a machine learning technique called support vector machine. We investigated improving the classification accuracy by increasing the number of data sets used to make the user concentration model. We increased the data sets from 30 to 180; consequently, the accuracy increased, with a maximum of about 80 % with 150 data sets. This indicates that our controller is able to accurately classify unstable data and can control a robot using brain waves from a simple electroencephalograph.

2P1-M07 簡易脳波計測器を用いた移動ロボットの制御システムの構築

Recently, simple instruments to measure the brain waves (EEG) is being developed and commercially available. In addition, a radio controlled helicopter controlled by the measured brain wave data is sold. The final goal of this study is to develop swarm robots controlled by EEG. Measuring instruments for medical use are very expensive and restrict human motion. Contrary, simple and easy to ware devices are commercially available that is inexpensive. Therefore, we choose a simple brain wave measuring instrument called B3band, which is able to send the brain wave data to PC through Bluetooth. In this paper, we describe a concept of our control system using B3band, and examples of recorded data. Also we introduce a mobile robot which has two driving wheels with RC servos.

Association Mining of the Brain Data: An EEG Study

Many neuroscience researchers have explored how various parts of the brain are connected. No one has performed association mining using the brain data. In this study, we used SAS® Enterprise MinerTM 7.1 for association mining of the brain data collected by a 14-channel EEG device. An application of the association mining technique is presented in this novel context of brain activities and by linking our results to theories of cognitive neuroscience. The brain waves are collected while a user processes the information about Facebook, the most well known social networking site. The data is cleaned using Independent Component Analysis via an open source MATLAB package. Next, by applying the LORETA algorithm, activations at a fraction of a second are recorded. The data is codified into transactions to perform association mining. Results showing how various parts of brain that get excited while processing the information are reported. This study provides preliminary insights into how brain wave data may be analyzed by commonly available data mining techniques to enhance a researcher’s understanding of brain activation patterns.

Applying the Bispectral Analysis on Widespread Diffuse Cross-Frequency Interactive Effects.

The aim of this paper is applying the bispectral analysis on widespread diffuse cross-frequency interactive effects. The event-related potentials (ERPs) research method was used in this study and it could collect the widespread diffuse cross-frequency from mild cognitive impairment (MCI) patients' brain wave. In this study, the brain wave data were collected from 12 MCI subjects, 12 healthy elderly, and 12 healthy young. The findings showed that the decreased interhemispheric coherence of 8.8 Hz for MCI compared with healthy elderly in the central-parietal cortex to respective surrounding sites and each MCI subject showed significantly widespread diffuse pattern of cross-frequency interactions in comparison with the healthy controls in the left central-parietal and right frontal. This study provides some explanation and suggestions for these findings.

Influence of polling technologies on student engagement: An analysis of student motivation, academic performance, and brainwave data

This study compared clicker technology against mobile polling and the Just-in-Time Teaching (JiTT) strategy to investigate how these methods may differently affect students' anxiety, self-efficacy, engagement, academic performance, and attention and relaxation as indicated by brainwave activity. The study utilized a quasi-experimental research design. To assess the differences between the effects of clickers and mobile polling, the study collected data from two courses at a large research university in Taiwan in which 69 students used either clickers or mobile polling. The results showed that mobile polling along with the JiTT strategy and in-class polls reduce graduate students' anxiety, improve student outcomes in an environment comprising both graduate and undergraduate students, and increase students' attention during polling. However, brainwave data revealed that during the polling activities, students' attention in the clicker and mobile polling groups respectively increased and decreased. Students nowadays do not find smartphones a novelty; however, incorporating them into class is still a potentially effective way to increase student attention and provide a direct way for instructors to observe the learning effects of lectures and improve their teaching approach on that basis.

Towards a Meditation Brain State Model for Scientific Study of Chan

Meditation has clear effects in bringing practitioners’ mind into a tranquil state and promoting both the mental and the physical health. The effect of meditation is measurable. The authors propose to establish a model for meditation state by applying modern experimental sciences to brain wave data. The authors start with a project that aims to create an application that takes electroencephalographic (EEG) data and exposes it to various analytical techniques so the resultant brain states can be studied and predicted. The authors present explanations of the design and implementation offered herein. Furthermore, a summary of the application’s functionality is elucidated. Upon completion, the authors anticipate that this software can be used to produce important and dependable conclusions about a given subject’s brain states and correlate that to an identified physical or psychological activity. Although their project is still in an early stage towards a model for meditation, through these studies, the authors believe they will be able to make meditation a beneficial practice to promote human’s life in modern society.

Development of 16-channels Compact EEG System Using Real-time High-speed Wireless Transmission

For the convenience of people with disability and for normal people, a demand for intelligent interfaces is ever increasing and therefore related studies are actively being conducted. Recently a study is being conducted to develop an interface through face expression, movement of the body and eye movements, and further more active attempts to use electrical signals(brainwave, electrocardiogram, electromyogram) measured from the human body is also actively being progressed. In addition, the development and the usage of mobile devices and smart devices are promoting these research activities even more. The brainwave is measured by electrical activities between nerve cells in the cerebral cortex using scalp electrodes. The brainwave is mainly used for diagnosis and treatment of diseases such as epilepsy, encephalitis, brain tumors and brain damage. As a result, the brainwave measurement methods and analytical methods were developed. Interface using the brainwave will not go through language or body behavior which is the result of the information processed by the brain but will pass directly to the system providing a brain-computer interface (BCI). This is possible because a variety of the brainwave appears depending on the human’s physical and mental state. Using the brainwave with the intelligent brain-computer interface or combining it with mobile devices and smart devices, regardless of space constraints, the brainwave measurement should be possible.[4,7] In this study, in order to measure the brainwave without spatial constraint, 16 channel compact brainwave measurements system using a high-speed wireless communications were designed. It was designed with a 16 channel to classify the various brainwave patterns that appear and for estimating the location of the nerve cells that triggered the brainwave. And in order to transmit the brainwave data within the channel without loss, a high-speed wireless communication must be possible that can enable a high-speed wireless transmission more sufficient than the Bluetooth, therefore, 802.11 compliant Wi-Fi communication methods were used to transfer the data to the PC. In addition, by using an analog front-end IC having a single-chip configuration with real-time digital filters, the miniaturization of the system was implemented and in order to verify the system Eye-blocking was used to observe the changes in the EEG signal.