Comparison of linear, nonlinear, and feature selection methods for EEG signal classification

TLDR

BCI operation relies on accurate classification of multichannel EEG, yet the high dimensionality and noise of EEG may diminish the advantage of nonlinear over linear classifiers. The study aims to design EEG representations and classifiers for BCI by extracting complex spatial and temporal patterns from noisy, multidimensional EEG data. The authors compare linear discriminant analysis, neural networks, and support vector machines, and apply a genetic‑algorithm‑based feature selection to EEG during finger movement. Nonlinear classifiers yield only slightly better performance than linear discriminant analysis, and genetic‑algorithm‑based feature selection shows preliminary promise for EEG finger‑movement classification.

Abstract

The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.