Skip to main content

Utilizing gamma band to improve mental task based brain-computer interface design

Palaniappan, Ramaswamy (2006) Utilizing gamma band to improve mental task based brain-computer interface design. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14 (3). pp. 299-303. ISSN 1534-4320. (doi:10.1109/TNSRE.2006.881539) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/TNSRE.2006.881539

Abstract

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.

Item Type: Article
DOI/Identification number: 10.1109/TNSRE.2006.881539
Additional information: Unmapped bibliographic data: LA - English [Field not mapped to EPrints] J2 - IEEE Trans. Neural Syst. Rehabil. Eng. [Field not mapped to EPrints] C2 - 17009489 [Field not mapped to EPrints] AD - IEEE, United Kingdom [Field not mapped to EPrints] AD - Department of Computer Science, University of Essex, Colchester, CO4 3SQ, United Kingdom [Field not mapped to EPrints] DB - Scopus [Field not mapped to EPrints] M3 - Article [Field not mapped to EPrints]
Uncontrolled keywords: Asymmetry ratio, Brain-computer interface (BCI), Gamma band, Mental task, Asymmetry ratio, Brain-computer interface (BCI), Gamma band, Mental task, Brain, Gamma rays, Human computer interaction, Natural frequencies, Patient rehabilitation, Electroencephalography, algorithm, article, artificial neural network, brain computer interface, controlled study, electroencephalogram, electroencephalography, gamma spectrometry, human, human experiment, mental performance, mental task, priority journal, task performance, training, Algorithms, Artificial Intelligence, Brain, Cognition, Communication Aids for Disabled, Electroencephalography, Equipment Design, Equipment Failure Analysis, Evoked Potentials, Humans, Pattern Recognition, Automated, Therapy, Computer-Assisted, User-Computer Interface
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Palaniappan Ramaswamy
Date Deposited: 15 Dec 2018 20:35 UTC
Last Modified: 30 May 2019 08:29 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70732 (The current URI for this page, for reference purposes)
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
  • Depositors only (login required):