EEG-Based Brain-Computer Interface for Tetraplegics

Movement-disabled persons typically require a long practice time to learn how to use a brain-computer interface (BCI). Our aim was to develop a BCI which tetraplegic subjects could control only in 30 minutes. Six such subjects (level of injury C4-C5) operated a 6-channel EEG BCI. The task was to move a circle from the centre of the computer screen to its right or left side by attempting visually triggered right- or left-hand movements. During the training periods, the classifier was adapted to the user's EEG activity after each movement attempt in a supervised manner. Feedback of the performance was given immediately after starting the BCI use. Within the time limit, three subjects learned to control the BCI. We believe that fast initial learning is an important factor that increases motivation and willingness to use BCIs. We have previously tested a similar single-trial classification approach in healthy subjects. Our new results show that methods developed and tested with healthy subjects do not necessarily work as well as with motor-disabled patients. Therefore, it is important to use motor-disabled persons as subjects in BCI development.

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EEG Based Thought Translator

International Journal of Biomedical and Clinical Engineering ◽

10.4018/ijbce.2013010105 ◽

2013 ◽

Vol 2 (1) ◽

pp. 50-62 ◽

Cited By ~ 5

Author(s):

N. Sriraam

Keyword(s):

Neural Network ◽

Brain Computer Interface ◽

Binary Classification ◽

Electric Activity ◽

Disabled Persons ◽

Computer Interface ◽

Frequency Domain Method ◽

Physically Disabled ◽

Left Hand ◽

Physically Disabled People

A brain computer interface is a communication system that translates brain activities into commands for a computer. For physically disabled people, who cannot express their needs through verbal mode (such as thirst, appetite etc), a brain-computer interface (BCI) is the only feasible channel for communicating with others. This technology has the capability of providing substantial independence and hence, a greatly improved quality of life for the physically disabled persons. The BCI technique utilizes electrical brain potentials to directly communicate to devices such as a personal computer system. Cerebral electric activity is recorded via the electroencephalogram (EEG) electrodes attached to the scalp measure the electric signals of the brain. These signals are transmitted to the computer, which transforms them into device control commands. The efficiency of the BCI techniques lies in the extraction of suitable features from EEG signals followed by the classification scheme. This paper focuses on development of brain-computer interface model for motor imagery tasks such as movement of left hand, right hand etc. Several time domain features namely, spike rhythmicity, autoregressive method by Burgs, auto regression with exogenous input, autoregressive method based on Levinson are used by varying the prediction order. Frequency domain method involving estimation of power spectral density using Welch and Burg’s method are applied. A binary classification based on recurrent neural network is used. An optimal classification of the imagery tasks with an overall accuracy of 100% is achieved based on configuring the neural network model and varying the extracted feature and EEG channels optimally. A device command translator finally converts these tasks into speech thereby providing the practical usage of this model for real-time BCI application.

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Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain–computer interface: three-class classification of rest, right-, and left-hand motor execution

Neurophotonics ◽

10.1117/1.nph.5.1.011008 ◽

2017 ◽

Vol 5 (01) ◽

pp. 1 ◽

Cited By ~ 34

Author(s):

Thanawin Trakoolwilaiwan ◽

Bahareh Behboodi ◽

Jaeseok Lee ◽

Kyungsoo Kim ◽

Ji-Woong Choi

Keyword(s):

Neural Network ◽

Near Infrared ◽

Brain Computer Interface ◽

High Accuracy ◽

Computer Interface ◽

Motor Execution ◽

Functional Near Infrared Spectroscopy ◽

Left Hand ◽

Right And Left Hand

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Validation of a brain-computer interface version of the digit symbol substitution test in healthy subjects

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2020.103729 ◽

2020 ◽

Vol 120 ◽

pp. 103729

Author(s):

Xiaogang Chen ◽

Nan Hu ◽

Yijun Wang ◽

Xiaorong Gao

Keyword(s):

Healthy Subjects ◽

Brain Computer Interface ◽

Computer Interface ◽

Digit Symbol Substitution Test ◽

Digit Symbol ◽

Digit Symbol Substitution ◽

Symbol Substitution

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Comparison of Using Mono-Polar and Bipolar Electroencephalogram (EEG) Electrodes for Detection of Right and Left Hand Movements in a Self-Paced Brain Computer Interface (BCI)

2007 Canadian Conference on Electrical and Computer Engineering ◽

10.1109/ccece.2007.186 ◽

2007 ◽

Cited By ~ 3

Author(s):

Ali Bashashati ◽

Rabab K. Ward ◽

Gary E. Birch

Keyword(s):

Brain Computer Interface ◽

Computer Interface ◽

Hand Movements ◽

Left Hand ◽

Electroencephalogram Eeg ◽

Right And Left Hand

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Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

Computational Intelligence and Neuroscience ◽

10.1155/2007/94561 ◽

2007 ◽

Vol 2007 ◽

pp. 1-9 ◽

Cited By ~ 106

Author(s):

Pablo Martinez ◽

Hovagim Bakardjian ◽

Andrzej Cichocki

Keyword(s):

Real Time ◽

Healthy Subjects ◽

High Speed ◽

High Performance ◽

Brain Computer Interface ◽

Computer Interface ◽

Optimal Frequency ◽

Computer Screen ◽

Near Future ◽

Bci System

We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI). The developed system allows a BCI user to navigate a small car (or any other object) on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.

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