scholarly journals Wheelchair controlled by human brainwave using brain-computer interface system for paralyzed patient

2021 ◽  
Vol 10 (6) ◽  
pp. 3032-3041
Author(s):  
Norasyimah Sahat ◽  
Afishah Alias ◽  
Fouziah Md Yassin
Keyword(s):  
Brain Computer Interface ◽  
Threshold Value ◽  
Computer Interface ◽  
Male Adult ◽  
Eye Blink ◽  
Human Attention ◽  
Blink Detection ◽  
Paralyzed Patient ◽  
The Right ◽  
Bci System

Integrated wheelchair controlled by human brainwave using a brain-computer interface (BCI) system was designed to help disabled people. The invention aims to improve the development of integrated wheelchair using a BCI system, depending on the ability individual brain attention level. An electroencephalography (EEG) device called mindwave mobile plus (MW+) has been employed to obtain the attention value for wheelchair movement, eye blink to change the mode of the wheelchair to move forward (F), to the right (R), backward (B) and to the left (L). Stop mode (S) is selected when doing eyebrow movement as the signal quality value of 26 or 51 is produced. The development of the wheelchair controlled by human brainwave using a BCI system for helping a paralyzed patient shows the efficiency of the brainwave integrated wheelchair and improved using human attention value, eye blink detection and eyebrow movement. Also, analysis of the human attention value in different gender and age category also have been done to improve the accuracy of the brainwave integrated wheelchair. The threshold value for male children is 60, male teenager (70), male adult (40) while for female children is 50, female teenager (50) and female adult (30).

scholarly journals A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5474 ◽  
Author(s):  
Dalin Yang ◽  
Trung-Hau Nguyen ◽  
Wan-Young Chung
Keyword(s):  
Steady State ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Home Automation ◽  
Eye Blink ◽  
Eye Blinks ◽  
Visually Evoked Potential ◽  
Automation Control ◽  
Bci System

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.

scholarly journals Electroencephalogram with Machine Learning for Estimation of Mental Confusion Level

2019 ◽  
Vol 9 (2) ◽  
pp. 761-765
Keyword(s):  
Machine Learning ◽  
Concentration Level ◽  
Brain Computer Interface ◽  
Quantitative Measure ◽  
Threshold Value ◽  
Machine Learning Algorithms ◽  
Computer Interface ◽  
Eeg Data ◽  
The Brain ◽  
Bci System

Estimating the mental state of an individual is crucial to many applications. A quantitative measure of the confusion one faces while doing a task can be useful in determining which subtask is the most difficult. This paper thus aims to develop an algorithm to estimate the confusion score using EEG signals collected using a Neurosky Mindwave Headset. Also, a full contextual audio based confusion score is generated to improve the system's resilience. In this paper, the final algorithm is used to propose an EEG based system to enable the UI/UX testing which can help in confusion estimation and thus provide a qualitative means to measure the attention and concentration level of people which can be extended to various applications. The raw EEG data collected from the device was used to calculate the confusion score using various Machine Learning algorithms. This brain computer interface (BCI) system can be extended for calculating the confusion score of a person which can be used for various applications such as teaching, child health monitoring, suicide prevention, mental health analysis etc. The brain computer interface thus calculates the confusion score and based on the threshold value of the attention and concentration level it performs certain actions such as sending messages and alerts to emergency contacts. This is further extended to solve the problem of Usability testing in Human Computer Interaction.

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.

scholarly journals Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mingwei Zhang ◽  
Yao Hou ◽  
Rongnian Tang ◽  
Youjun Li
Keyword(s):  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Covariance Matrices ◽  
Computer Interface ◽  
Spatial Covariance ◽  
Learning Machine ◽  
Low Dimensional ◽  
Bci System

In motor imagery brain computer interface system, the spatial covariance matrices of EEG signals which carried important discriminative information have been well used to improve the decoding performance of motor imagery. However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.

Home Automation by Brain-Computer Interface

2014 ◽  
pp. 502-510
Author(s):  
Eduardo G. Nieva ◽  
María F. Peralta ◽  
Diego A. Beltramone
Keyword(s):  
Frontal Lobe ◽  
Brain Computer Interface ◽  
Heating System ◽  
Threshold Value ◽  
Threshold Level ◽  
Computer Interface ◽  
Second Step ◽  
Electroencephalographic Signals ◽  
Commercial Device ◽  
The Brain

In the present work, the authors use the Brain Computer Interface technology to allow the dependent persons the utilization of the basic elements of their house, such as turning on and turning off lamps, rolling up and down a roller shutter, or switching on the heating system. For doing this, it is necessary to automate these devices and to centralize its managing in a platform, which constitutes a domotics system. In order to achieve this, the authors have used the MindWave NeuroSky ® commercial device. It is affordable, portable, and wireless, and senses and delivers the computer the electroencephalographic signals produced in the frontal lobe and the levels of attention, relaxation, and blinking to the computer. In order to determine the efficiency of the obtained signals a test software was designed, which verified the operation´s device with different persons. The authors conclude that the easiest way to control the attention levels is concentrating on a certain point, and the way to control the relaxation levels is by closing the eyes. As a second step, the authors develop a software that takes the signal from the EEG (Electro Encephalo Graphy) sensor, processes it, and sends signals via USB to an Arduino board, which is associated with electronics that complies the different tasks. The user chooses the action by managing the attention levels. When they are higher than a particular threshold value, the action is executed. In order to disable this action, the user must lower the threshold level and overcome it again. This is the simplest and fastest way to handle, but it brings several problems: if the user concentrates for any other reason and this signal exceeds the threshold, it causes the activation of an involuntary action. To solve this problem, the authors use a three variables combination that can become independent of each other thru training properly. These variables are attention, meditation, and blink. When you comply with the three simultaneous previously established conditions, the action is executed, and when they return to fulfill the conditions, the action is deactivated. The software also has the feature of personalizing its conditions, so it can be best for any user, even a novice one.

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