Enhancing Temporal Classification of AAR Parameters in EEG single-trial analysis for Brain-Computer Interfacing

Author

Dharwarkar, G.S. ; Basir, O.

Author_Institution

Electr. & Comput. Eng., Waterloo Univ., Ont.

fYear

2006

Firstpage

5358

Lastpage

5361

Abstract

Adaptive autoregressive (AAR) coefficients provide dynamic spectral information in EEG single-trial analysis. In this paper we propose a temporal evidence accumulation framework to enhance classification of AAR features. The results for a single subject, using 280 trials, indicate distinct improvements over a conventional method of temporal classification. We illustrate how the framework is applicable to AAR features, as well as to wavelet features as reported in Lemm et al., (2004). These findings put the two time-frequency features on equal footing for comparison in this context

Keywords

autoregressive processes; electroencephalography; handicapped aids; medical signal processing; signal classification; AAR parameters; EEG single-trial analysis; adaptive autoregressive coefficients; brain-computer interfacing; dynamic spectral information; temporal classification enhancement; temporal evidence accumulation framework; time-frequency features; wavelet features; Biomedical engineering; Brain modeling; Context modeling; Data analysis; Electroencephalography; Pattern analysis; Predictive models; Protocols; Signal analysis; Synchronous motors; Brain-computer interface (BCI); adaptive autoregressive model (AAR); electroencephalogram (EEG); motor imagery;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1615692

Filename

1615692