Utilizing Gamma Band to Improve Mental Task Based Brain-Computer Interface Design

A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental...

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Bibliographic Details
Published inIEEE transactions on neural systems and rehabilitation engineering Vol. 14; no. 3; pp. 299 - 303
Main Author Palaniappan, R.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.09.2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial Intelligence
Asymmetry
Asymmetry ratio
Backpropagation algorithms
Biological neural networks
Brain - physiology
Brain computer interfaces
brain-computer interface (BCI)
Cognition - physiology
Communication Aids for Disabled
Design
Design methodology
Electrodes
Electroencephalography
Electroencephalography - methods
Equipment Design
Equipment Failure Analysis
Evoked Potentials - physiology
Feature extraction
Frequency
gamma band
Humans
mental task
Neural networks
Pattern Recognition, Automated - methods
Scalp
Signal design
Studies
Therapy, Computer-Assisted - instrumentation
Therapy, Computer-Assisted - methods
User-Computer Interface
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ISSN1534-4320
1558-0210
DOI10.1109/TNSRE.2006.881539

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Summary:A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that 1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; 2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method
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ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2006.881539