Automatic frequency bands segmentation using statistical similarity for power spectrum density based brain computer interfaces

Tian Lan, Deniz Erdogmus, Misha Pavel, Santosh Mathan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Power spectrum density (PSD) of electroencephalogram (EEG) signals is a widely used feature for Brain Computer Interfaces (BCI). Usually, PSD features are integrated over different frequency bands, such as delta, theta, alpha, beta, gamma, which are based on well-established interpretations of EEG signals in prior experimental and clinical contexts. However, these predefined frequency bands do not necessarily relate to the optimal features for various BCI applications. In this paper, we propose an alternative feature dimensionality reduction method, which automatically determines the optimal number and the range of frequency bands. We applied the proposed method on EEG classification in the context of Augmented Cognition (AugCog) using BCI. The experimental results show that the proposed method can extract more robust features than features manually extracted from predefined frequency bands.

Original languageEnglish (US)
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
Pages4650-4655
Number of pages6
StatePublished - Dec 1 2006
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: Jul 16 2006Jul 21 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Other

OtherInternational Joint Conference on Neural Networks 2006, IJCNN '06
CountryCanada
CityVancouver, BC
Period7/16/067/21/06

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ASJC Scopus subject areas

  • Software

Cite this

Lan, T., Erdogmus, D., Pavel, M., & Mathan, S. (2006). Automatic frequency bands segmentation using statistical similarity for power spectrum density based brain computer interfaces. In International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 4650-4655). [1716745] (IEEE International Conference on Neural Networks - Conference Proceedings).