A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces

Abstract

Event-related desynchronization/synchronization patterns during right/left motor imagery (MI) are effective features for an electroencephalogram-based brain-computer interface (BCI). As MI tasks are subject-specific, selection of subject-specific discriminative frequency components play a vital role in distinguishing these patterns. This paper proposes a new discriminative filter bank (FB) common spatial pattern algorithm to extract subject-specific FB for MI classification. The proposed method enhances the classification accuracy in BCI competition III dataset IVa and competition IV dataset IIb. Compared to the performance offered by the existing FB-based method, the proposed algorithm offers error rate reductions of 17.42% and 8.9% for BCI competition datasets III and IV, respectively.

Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

“…The results of all analyses are then combined to form the final response (see Figure 4). Similar approaches have been proposed in [10,65,66]. An extension to the multiclass problem can be found in [67].…”

“…Among them, motor imagery (MI)-based BCI systems seem to be the most promising option [6,[8][9][10]. In MI-based BCI systems, the subject is asked to imagine the movement of different parts of his or her body, such as the hands or the feet.…”

On robust spatial filtering of EEG in nonstationary environments

“…Wavelet CSP methods have been considered in (Mousavi et al, 2011;Robinson et al, 2013) whereas the authors of (Falzon et al, 2012) improve the discriminative capability of CSP by taking into account both the amplitude and phase components of the EEG signal. A CSP variant directly optimizing the discriminativity of the features was proposed in (Thomas et al, 2009;Fattahi et al, 2013). A recently published approach (Li et al, 2013) learns spatial filters by considering signal propagation and volume conduction effects.…”

Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface

“…So far, two types of approaches have been mainly proposed to fix the problem of filter band selection. One is simultaneous optimization of spectral filters within the CSP [26,27,29] while another one is selection of significant CSP features from multiple frequency bands [24,28,30].…”

“…Higher classification accuracy was achieved by FBCSP over SBCSP. Subsequently, Thomas et al [30] proposed a discriminant FBCSP (DFBCSP) using Fisher ratio to select subject-specific filter bands instead of fixed ones, which enhanced accuracy of the FBCSP.…”