Sub-band Common Spatial Pattern (SBCSP) for Brain-Computer Interface

Abstract

Abstract-Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For Electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in µ and/or β rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called Sub-band Common Spatial Pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into Linear Discriminant Analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: Recursive Band Elimination (RBE) and Meta-Classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process.

Filter Selection Method Using CSP and LDA for Filter-bank based BCI Systems

“…. 이러한 문제를 해결하기 위해 필터 뱅크 기반의 시스템인 sub-band common spatial pattern(SBCSP) [11] , filter bank common spatial pattern(FBCSP) [12] , 그리고 discriminative filter bank common spatial pattern (DFBCSP) [13] 이 …”

Multiband Common Spatial Pattern based EEG Classification for Brain-Computer Interface

“…Another approach called SPECtrally weighted common spatialpattern (SPEC-CSP) algorithm [14] optimizes the temporal filter in the frequency domain and after that the spatial filter in an iterative method [15]. However, due to the inherent nature of optimization problem, the solution of filter coefficients significantlydepends on the selection of initial parameters [11].…”

“…The effectiveness of the spatial filters depends on its subject specificfrequency band i.e. the performance varies with subjects as well as frequency bands.If the EEG signalsis unfiltered or have been filtered with badly chosen frequency rangethen the classification of that signals using CSP shows poor accuracies [11]. Consequently, subjectspecific frequency bands are generally used with the CSP algorithm [12].…”

“…Sub-band CSP (SBCSP) method [11] was alternatively proposed and has been shown better classification accuracy compared against CSSP and CSSSP. In this method publicly available dataset from BCI competition III in 2005 has been used.…”

Classifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor Imagery

“…The success of CSP in BCI application greatly depended on the proper selection of subject specific frequency bands. In the literature, common sparse spectral spatial pattern (CSSSP) (Dornhege et al, 2006) sub band CSP (SBCSP) (Novi et al, 2007); Filter bank CSP (FBCSP) (Ang et al, 2008) and adaptive FBCSP (Thomas et al, 2008) have been proposed for choosing the optimal frequency band automatically.…”