Abstract
The common spatial pattern analysis (CSP), a frequently utilized feature extraction method in brain-computer-interface applications, is believed to be time-invariant and sensitive to noises, mainly due to an inherent shortcoming of purely relying on spatial filtering. Therefore, temporal/spectral filtering which can be very effective to counteract the unfavorable influence of noises is usually used as a supplement. This work integrates the CSP spatial filters with complex channel-specific finite impulse response (FIR) filters in a natural and intuitive manner. Each hybrid spatial-FIR filter is of high-order, data-driven and is unique to its corresponding channel. They are derived by introducing multiple time delays and regularization into conventional CSP. The general framework of the method follows that of CSP but performs better, as proven in single-trial classification tasks like event-related potential detection and motor imagery.
Highlights
The successfulness of common spatial pattern analysis (CSP) in the brain-computer interface applications such as motor imagery (MI) and event-related potential (ERP) detection has received considerable attentions [1,2,3,4,5]
Pcv and Pfix achieved 2.7% and 3.7% higher average accuracy, respectively. Their performances were more superior in rapid serial visual presentation (RSVP) experiments, where the achieved accuracies were 7.9% and 8.1% higher than CSP, respectively
The general performances of common sparse spectral spatial pattern (CSSSP), bilinear common spatial pattern (BCSP), and analytic common spatial patterns (ACSP) were worse than that of CSP. This phenomenon was absent in the scenario of RSVP experiments, where BCSP obtained 5.8% higher average accuracy in compar
Summary
The successfulness of common spatial pattern analysis (CSP) in the brain-computer interface applications such as motor imagery (MI) and event-related potential (ERP) detection has received considerable attentions [1,2,3,4,5]. The filters obtained by CSP heavily rely on spatial projections It technically underrates the temporal/spectral information of electroencephalogram (EEG), which plays an important role in feature extraction. Unlike CSSP and CSSSP which are characterized by time delays, spectrally weighted common spatial patterns (SPEC-CSP) [11] and iterative spatio-spectral patterns learning (ISSPL) [12] introduce the linear time-invariant temporal filter and circulant temporal filter matrix, respectively. Both of them use Fourier transform so that the optimization of temporal filters can be carried out in the spectral domain. Another interesting variant of CSP, namely analytic common spatial patterns (ACSP), implements Hilbert transform into CSP to extract complex-valued filters which already contain temporal information [13,14]
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