A multi-class brain-computer interface with SOFNN-based prediction preprocessing

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

5 Citations (Scopus)

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
Original languageEnglish
Title of host publicationUnknown Host Publication
Pages3695-3702
Number of pages8
Publication statusPublished - Jun 2008
EventIEEE World Congress on Computational Intelligence - Hong Kong, China
Duration: 1 Jun 2008 → …

Conference

ConferenceIEEE World Congress on Computational Intelligence
Period1/06/08 → …

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