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

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 14; no. 3; pp. 299 - 303
• Main Author: Palaniappan, R.
• Format: Journal Article
• Language: English
• 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
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2006.881539

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