Single-channel electroencephalogram analysis using non-linear subspace techniques

In this work, we propose the correction of univariate, single channel EEGs using projective subspace techniques. The biomedical signals which often represent one dimensional time series, need to be transformed to multi-dimensional signal vectors for the latter techniques to be applicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. We propose the application of two non-linear subspace techniques to the obtained multidimensional signal. One of the techniques consists in a modified version of Singular Spectrum Analysis (SSA) and the other is kernel Principal Component Analysis (KPCA) implemented using a reduced rank approximation of the kernel matrix. Both nonlinear subspace projection techniques are applied to an electroencephalogram (EEG) signal recorded in the frontal channel to extract its prominent electrooculogram (EOG) interference.