Invariant common spatial pattern advanced feature extraction in mu rhythms of EEG signals

Abstract

Classification of limbs motion intention based on electroencephalogram (EEG) is one of the major subjects of brain-computer interface (BCI). EEG signals contained not only noise artefacts, but also variations within and across sessions. To reduce this unnecessary information in EEG signal processing, we applied an extension version of common spatial pattern (CSP). The original CSP has been widely used to extract relevant spatial features from EEG signals. However, one of the CSP limitations is occurred when it concentrated on artifacts and variances sources, which exceeded the variance of endogenous component of the brain, instead of extracting the sources that provide subject's intention. Therefore, we proposed to add more parameter for separating invariance and variance in EEGs. To reduce the variant parts of signal in EEGs, we applied invariant common spatial pattern (iCSP) to maximize one class in the same time minimize other class and invariant sources. Based on neurophysiological knowledge, we made some strong weights to special electrodes related to our experimental paradigm. The raw EEG signals were recorded by auditory stimuli with 3 different frequencies cued for real hand movement and ignored noise. The right or left actual hand movements-based EEG signal states were classified by using linear discriminant analysis (LDA).