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EEG Based Thought Translator: A BCI Model for Paraplegic Patients

EEG Based Thought Translator: A BCI Model for Paraplegic Patients

N. Sriraam
Copyright: © 2013 |Volume: 2 |Issue: 1 |Pages: 13
ISSN: 2161-1610|EISSN: 2161-1629|EISBN13: 9781466630628|DOI: 10.4018/ijbce.2013010105
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MLA

Sriraam, N. "EEG Based Thought Translator: A BCI Model for Paraplegic Patients." IJBCE vol.2, no.1 2013: pp.50-62. http://doi.org/10.4018/ijbce.2013010105

APA

Sriraam, N. (2013). EEG Based Thought Translator: A BCI Model for Paraplegic Patients. International Journal of Biomedical and Clinical Engineering (IJBCE), 2(1), 50-62. http://doi.org/10.4018/ijbce.2013010105

Chicago

Sriraam, N. "EEG Based Thought Translator: A BCI Model for Paraplegic Patients," International Journal of Biomedical and Clinical Engineering (IJBCE) 2, no.1: 50-62. http://doi.org/10.4018/ijbce.2013010105

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Abstract

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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