scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

scholarly journals Deep Learning on Game Addiction Detection Based on Electroencephalogram

2021 ◽  
Vol 5 (3) ◽  
pp. 963
Author(s):  
Lalu Arfi Maulana Pangistu ◽  
Ahmad Azhari
Keyword(s):  
Brain Activity ◽  
Late Adolescence ◽  
Wave Activity ◽  
Test Results ◽  
Brain Wave ◽  
Brain Waves ◽  
Game Addiction ◽  
Brain Wave Activity ◽  
Electroencephalogram Eeg ◽  
The Brain

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence. 

scholarly journals BRAIN COMPUTER INTERFACE APPLICATION IN STROKE DISEASE DIAGNOSIS

2016 ◽  
Vol 4 (7) ◽  
pp. 94-101
Author(s):  
Igwe J. S. ◽  
Inyiama ◽  
OgbuNwani Henry
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Research Work ◽  
Disease Diagnosis ◽  
Computer Interface ◽  
Theta Wave ◽  
Brain Signals ◽  
The Status ◽  
Electroencephalogram Eeg ◽  
The Brain

Every discovery is geared towards problem solving. This is manifested by the advent of brain computer interface (BCI). Brain computer interface (BCI) is a field of study concern with the detection and utilization of brain signals in establishing the communication path between the brain and the computer system. The knowledge of this science has helped in no small measure in providing solutions to several challenges befalling man and his environment. In this paper, we explored those areas where BCI has proved useful and pointed out as well its possible application in diagnosis of stroke disease. The discourse was centered on detection of electrochemical signals from the brain called electroencephalogram (EEG). The research work also highlighted the technique of recording brain activity via electroencephalogram and using it in making deduction on the status of stroke attack on individual. This can either be normal or abnormal. The presence of delta or theta wave in an awaked adult suggests an abnormal situation. While the observance of alpha, beta and gamma waves are interpreted as normal.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

scholarly journals FRAMEWORK OF CONTROLLING 3D VIRTUAL HUMAN EMOTIONAL WALKING USING BCI

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Faris Amin M. Abuhashish ◽  
Hoshang Kolivand ◽  
Mohd Shahrizal Sunar ◽  
Dzulkifli Mohamad
Keyword(s):  
Signal Processing ◽  
Augmented Reality ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Virtual Human ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
New Framework

A Brain-Computer Interface (BCI) is the device that can read and acquire the brain activities. A human body is controlled by Brain-Signals, which considered as a main controller. Furthermore, the human emotions and thoughts will be translated by brain through brain signals and expressed as human mood. This controlling process mainly performed through brain signals, the brain signals is a key component in electroencephalogram (EEG). Based on signal processing the features representing human mood (behavior) could be extracted with emotion as a major feature. This paper proposes a new framework in order to recognize the human inner emotions that have been conducted on the basis of EEG signals using a BCI device controller. This framework go through five steps starting by classifying the brain signal after reading it in order to obtain the emotion, then map the emotion, synchronize the animation of the 3D virtual human, test and evaluate the work. Based on our best knowledge there is no framework for controlling the 3D virtual human. As a result for implementing our framework will enhance the game field of enhancing and controlling the 3D virtual humans’ emotion walking in order to enhance and bring more realistic as well. Commercial games and Augmented Reality systems are possible beneficiaries of this technique.

scholarly journals A Robust Emotion Extraction System from EEG Signal Dataset using Machine Learning

2021 ◽  
pp. 319-330
Author(s):  
Muthulakshmi P ◽  
Gopika R
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Extraction System ◽  
Eeg Signal ◽  
Eeg Asymmetry ◽  
Non Invasive ◽  
Brain Signals ◽  
Human Stress ◽  
Invasive Method ◽  
The Brain

The project entitled “A Robust Emotion Extraction System from EEG signal Dataset using Machine Learning” has been developed using MATLAB. The brain activity produces the different kinds of signals like electrical and magnetic signals. This activity can be recorded using different kind of approaches, which are normally classified as invasive and non-invasive. In invasive methods surgical intervention are made to implant certain device in the brain whereas in non-invasive methods no such intervention is made. Among the different non-invasive methods, Electroencephalography is one of the most commonly used methods to record the brain signals. EEG is regarded as direct and simple non-invasive method to record the brain electrical activity. Current flow in the neurons of the brain is represented as voltage fluctuation (EEG). EEG waves which can be represented as the signal over time are recorded by the electrodes places on scalp over the brain. EEG Asymmetry and Spectral Centroids techniques in extracting unique features for human stress. In our proposed work we have to classify the EEG signal whether that is stress or not. In our proposed work we will extract the features and optimizing Using Genetic Algorithm then we finally classify the EEG signal.

Eyeblink Robot Control Using Brain-Computer Interface for Healthcare Applications

2019 ◽  
Vol 10 (2) ◽  
pp. 38-50
Author(s):  
Sravanth K. Ramakuri ◽  
Premkumar Chithaluru ◽  
Sunil Kumar
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Green Light ◽  
Muscular Activity ◽  
Computer Interface ◽  
Healthcare Applications ◽  
Brain Wave ◽  
Central Organ ◽  
The Brain ◽  
Wave Device

The human brain is the central organ of the human system. Many people in the world cannot move on their own and can't control things on their own. A person whose brain is active can control things using the neuro-controlled robot car. It is interesting to all types of people to measure their concentration and piece level of mind with the neuro sky mind wave device. One can easily control the robot's movements by simply blinking eyes; the robot's speed will be according to the subject's attention levels. The neuro sky mind wave device digitizes brain wave signals to power the user-interface of the computers, game, and health application. The neuro sky mind wave device will measure brain waves from the forehead. The paper aims to control a robot using the brain-computer interface concept without any muscular activity controlling healthcare applications directions. The brain activity is recorded with the neuro sky mind wave device's help, and the attention values are sent to the Arduino with the help of the HC-05 Bluetooth module. Arduino is programmed so that if the attention values between 0-29 and the person are relaxed, the green light will glow for the feedback.

scholarly journals A comparative study on feature extraction and classification of mind waves for brain computerinterface (BCI)

2018 ◽  
Vol 7 (1.9) ◽  
pp. 132
Author(s):  
Manjula K ◽  
M B.Anandaraju
Keyword(s):  
Feature Extraction ◽  
Brain Activity ◽  
Non Invasive ◽  
Brain Signals ◽  
Control Command ◽  
Signal Processing Algorithms ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg ◽  
Bci System

Brain Computer Interfacing (BCI) is a methodology which imparts a path for communication from external world using brain signals through computer. BCI identifies the specific patterns in a person’s changing brain activity to initiate control which relates to the person’s intention. The BCI system paraphrases these signal patterns into meaningful control command. In evolving BCI system, numerous signal processing algorithms are proposed. Non-invasive Electroencephalogram (EEG) signals or mind waves are used to extract the distinguished features and further they are classified choosing an appropriate classifier. A study on different feature extraction & Classification algorithms is used in EEG-based BCI exploration and to identify their distinct properties. This paper proposes different methodologies of feature extraction and feature Classification. It also addresses the methods and technology adapted in every phase of the EEG signal processing.This comparative survey also helps in selecting suitable algorithm for the development and accomplishment of further classification of signals.

EEG Classification for BCI Based on CSP and SVM-GA

2013 ◽  
Vol 459 ◽  
pp. 228-231 ◽  
Author(s):  
Hao Yang ◽  
Song Wu
Keyword(s):  
Classification Performance ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Data Set ◽  
Linear Discriminant ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is generally used in Brain-Computer Interface (BCI) applications to measure the brain signals. However, the multichannel EEG signals characterized by unrelated and redundant features will deteriorate the classification accuracy. This paper presents a method based on common spatial pattern (CSP) for feature extraction and support vector machine with genetic algorithm (SVM-GA) as a classifier, the GA is used to optimize the kernel parameters setting. The proposed algorithm is performed on data set Iva of BCI Competition III. Results show that the proposed method outperforms the conventional linear discriminant analysis (LDA) in average classification performance.

scholarly journals How Can We Train The Brain To Help Stroke Patients?

2021 ◽  
Vol 9 ◽  
Author(s):  
Christoph Guger ◽  
Marc Sebastián-Romagosa ◽  
Woosang Cho ◽  
Tim Von Oertzen ◽  
Kyousuke Kamada ◽  
...  
Keyword(s):  
Standardized Tests ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Hand Movement ◽  
Computer Interface ◽  
Stroke Therapy ◽  
Research Groups ◽  
Stroke Patients ◽  
Brain Waves ◽  
The Brain

Many people who have had a stroke have trouble moving, even after therapy with the best experts and methods. New ways to make stroke therapy more effective could help people recover more effectively. Some research groups have developed brain-computer interface (BCI) systems that can measure when a stroke patient imagines hand movement by recording brain waves. We developed a BCI that used each patient’s brain activity to control a muscle stimulator and a monitor during therapy. The patients got rewarding feedback during therapy when they imagined a movement correctly. We tested 51 patients, some of whom had a stroke many years ago. Forty nine patients improved after the therapy, based on the results of standardized tests. Therefore, BCI-based therapy could help some stroke patients. We think there will be further advances in the next several years that will lead to more effective therapies using BCIs.

scholarly journals Electroencephalogram based Brain Computer Interface System Analysis

2020 ◽  
Vol 9 (3S) ◽  
pp. 432-439
Keyword(s):  
System Analysis ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Speech Impairment ◽  
Digital Form ◽  
Brain Signals ◽  
Highly Sensitive ◽  
History Of ◽  
The Brain

Electroencephalogram is the study of electrical signals of the brain recorded using a mesh like structure containing electrodes that is placed on the scalp. The history of EEG dates more than a century back, when the brains of rabbits and monkeys were studied and almost 50 years later in the year 1924, the first ever brain activity of human was noted by famed psychiatrist and physiologist Hans Berger from Germany. EEG based systems that can communicate with the brain are categorized as Brain Computer Interface i.e. BCI. The electrodes read the brain signals, amplifies them in order to be studied more accurately by the machine send them to machine after converting it into digital form. With the recent changes in technology better electrodes are being used which can catch highly sensitive signals as well.[1][2] EEG based BCI systems can change the world for many people as it holds so much power if thought properly. This paper reviews the EEG functioning and some innovations in BCI and also proposes ideas about potential help for people suffering from hearing and speech impairment.

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