Creating a nonparametric brain-computer interface with neural time-series prediction preprocessing.
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.
Coyle
Damien
D
Intelligent Syst. Eng. Lab., Ulster Univ, Derry, Nothern Ireland, BT48 7JL, UK. dh.coyle@ulster.ac.uk
McGinnity
Thomas M
TM
Prasad
Girijesh
G
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D009044:Motor Cortex / Q000502:physiology*; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013647:Task Performance and Analysis; D014584:User-Computer Interface