Electroencephalographic Denoising and Classification by Using Power Spectrum Density Based Independent Component Analysis and Common Spatial Pattern

摘要

With the participation of 12 volunteers, the off-line application of independent component analysis for automatic artefacts removal based on power spectral density is investigated. By using the "range" values of the power spectra of the independent components within the frequency range of 2 to 8 Hz along with the integral values of the independent components in the range of 8 to 30 Hz, artificial independent components are automatically marked and removed. The artefact-free electroencephalographic signal is further classified using the method of common spatial pattern. It is found that the modification of the conventional common spatial pattern can result in a higher imagery task classification.