Creating a nonparametric brain-computer interface with neural time-series prediction preprocessing.

Full Abstract

The issue of subject-specific parameter selection in an electroencephalogram (EEG)-based brain-computer interface (BCI) is tackled in this paper. Hjorth- and Barlow-based feature extraction procedures (FEPs) are investigated along with linear discriminant analysis (LDA) for classification. These are well-known nonparametric FEPs but their simplicity prevents them from matching the performance of more complex FEPs. Neural time-series prediction preprocessing (NTSPP) has been shown to enhance the separability of both time- and frequency-based features and is used in this work to improve the applicability of these FEPs. NTSPP uses a number of prediction modules (PMs) to perform m-step ahead prediction of EEG time-series recorded whilst subjects perform motor imagery-based mental tasks. Depending on the PMs, the NTSPP framework normally requires subject-specific parameters to be predefined. In this work each PM is a self-organizing fuzzy neural network (SOFNN). The SOFNN has a self-organizing structure and good nonlinear approximation capabilities however; a number of parameters must be defined prior to training. This is problematic therefore the practicality of a general set of parameters, previously selected via a sensitivity analysis (SA), is analyzed. The results indicate that a general set of NTSPP parameters may provide the best results and therefore a fully nonparametric BCI may be realizable.

Author information

Author/s: Coyle, Damien (D); McGinnity, Thomas M (TM); Prasad, Girijesh (G);

Affiliation: Intelligent Syst. Eng. Lab., Ulster Univ, Derry, Nothern Ireland, BT48 7JL, UK. dh.coyle(-atsign-)ulster.ac.uk

Journal and publication information

Publication Type: Evaluation Studies; Journal Article

Journal: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference (Conf Proc IEEE Eng Med Biol Soc), published in United States. (Language: eng)

Reference: 2006-; vol 1 (issue ) : pp 2183-6

Dates: Created 2007/10/23; Completed 2008/03/17;

PMID: 17946502, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: 18 Feb 2009 00:00:00)

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