scholarly journals A novel framework combining BCI and IOT for the detection of activity of the brain

2021 ◽  
pp. 053-059
Author(s):  
Karthiga B ◽  
Rekha M
Keyword(s):  
Brain Research ◽  
Brain Computer Interface ◽  
Wearable Sensors ◽  
Computer Interface ◽  
Software Frameworks ◽  
Major Development ◽  
True State ◽  
The Status ◽  
Electroencephalogram Eeg ◽  
The Brain

Virtual brain research is accelerating thedevelopment of inexpensive real-time Brain Computer Interface (BCI). Hardware improvements that increase the capability of Virtual brain analyse and Brain Computer wearable sensors have made possible several new software frameworks for developers to use and create applications combining BCI and IoT. In this paper, we complete a survey on BCI in IoT from various perspectives; including Electroencephalogram (EEG) based BCI models, machine learning, and current active platforms. Based on our investigations, the main findings of this survey highlights three major development trends of BCI, which are EEG, IoT, and cloud computing. Using this it is completely useful for finding the true state of whether the brain is alive or dead. If it is alive, then the activity of the brain is monitored and stored. Through this anyone can come to conclusion that whether the action done is legal or illegal. And this has an advantage for 2 scenarios. First is for AUTISM affected people and secondly Forgery in asset documents. And if any changes in the status of the brain then it will be send to the specific person in their relation using SMS & Email id.

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.

scholarly journals Application of Convolutional Neural Network Method in Brain Computer Interface

2021 ◽  
Vol 2078 (1) ◽  
pp. 012044
Author(s):  
Lingzhi Chen ◽  
Wei Deng ◽  
Chunjin Ji
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Network Method ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

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 Dry Electrode Cap and Its Application in a Steady-State Visual Evoked Potential-Based Brain–Computer Interface

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1080 ◽  
Author(s):  
Xiaoting Wu ◽  
Li Zheng ◽  
Lu Jiang ◽  
Xiaoshan Huang ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
State Monitoring ◽  
Contact Impedance ◽  
Dry Electrodes ◽  
Dry Electrode ◽  
Electroencephalogram Eeg ◽  
The Brain

The wearable electroencephalogram (EEG) dry electrode acquisition system has shown great application prospects in mental state monitoring, the brain–computer interface (BCI), and other fields due to advantages such as being small in volume, light weight, and a ready-to-use facility. This study demonstrates a novel EEG cap with concise structure, easy adjustment size, as well as independently adjustable electrodes. The cap can be rapidly worn and adjusted in both horizontal and vertical dimensions. The dry electrodes on it can be adjusted independently to fit the scalp as quickly as possible. The accuracy of the BCI test employing this device is higher than when employing a headband. The proposed EEG cap makes adjustment easier and the contact impedance of the dry electrodes more uniform.

scholarly journals Automated Artifact Removal in EEG Signals of Brain Computer Interface using Wavelets and ICA

2019 ◽  
Vol 8 (12) ◽  
pp. 896-909
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Control Line ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Natural Sources ◽  
Control Signals ◽  
The Mind ◽  
Electroencephalogram Eeg ◽  
The Brain

A brain-computer interface (BCI) gives a correspondence channel that interconnects the mind with an outside device. The most generally utilized system for getting BCI control signals from the brain is the electroencephalogram (EEG). In the proposed paper, BCI framework towards an EEG chronicles are reviewed into and found that the expansion of a counterfeit motion toward it, which is brought about by eye flickers, eye development, muscle and cardiovascular commotion, just as non-natural sources (e.g., control line clamor). According to the writing survey it is discovered that these issues can be overwhelmed by utilizing mix of wavelet deterioration, independent component analysis (ICA), and thresholding

scholarly journals Brain–Computer Interfaces: Toward a Daily Life Employment

2020 ◽  
Vol 10 (3) ◽  
pp. 157 ◽  
Author(s):  
Pietro Aricò ◽  
Nicolina Sciaraffa ◽  
Fabio Babiloni
Keyword(s):  
User Interfaces ◽  
Review Paper ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Computer Interfaces ◽  
Brain Sciences ◽  
Technology Improvement ◽  
New Consumer ◽  
Electroencephalogram Eeg ◽  
The Brain

Recent publications in the Electroencephalogram (EEG)-based brain–computer interface field suggest that this technology could be ready to go outside the research labs and enter the market as a new consumer product. This assumption is supported by the recent advantages obtained in terms of front-end graphical user interfaces, back-end classification algorithms, and technology improvement in terms of wearable devices and dry EEG sensors. This editorial paper aims at mentioning these aspects, starting from the review paper “Brain–Computer Interface Spellers: A Review” (Rezeika et al., 2018), published within the Brain Sciences journal, and citing other relevant review papers that discussed these points.

scholarly journals Classify mental states from EEG signal using XGBoost algorithm

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Yihao Wang
Keyword(s):  
Virtual Reality ◽  
Teaching Method ◽  
Brain Computer Interface ◽  
Leading Edge ◽  
Mental States ◽  
Medical Rehabilitation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Electroencephalogram Eeg ◽  
The Brain

Brain-computer interface (BCI) is a leading-edge technique which allows the brain communicates with external devices. It has been applied in several fields, such as medical rehabilitation, virtual reality and so on. This invention introduces a technique that can be applied in education field to monitor and analyze users’ electroencephalogram (EEG) so that the mental states could be identified. The algorithm of classifier used XGBoost which combined Bayes, KNN and SVM in it and its accuracy could reach to 80%. By using this technique, teacher could obtain the concentration status of students in real time and adjust his or her teaching method or remind the student who is wandering.

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 The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

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