- Poster Presentation
- Open Access
Independent spatial patterns: an integration of common spatial patterns with independent component analysis for multi- class discrimination in EEG based brain-computer interfaces
© Naeem et al; licensee BioMed Central Ltd. 2010
- Published: 20 July 2010
- Independent Component Analysis
- Motor Imagery
- Movement Imagination
- Projection Matrice
- Common Spatial Pattern
Independent spatial patterns (ISP) aims to integrate common spatial patterns (CSP) and Independent component analysis (ICA) by utilizing the best features of the two methods and at the same time going round some of the caveats associated with them. In the case of ICA importance of components cannot be pre-determined. Principal component analysis (PCA) dimensional reduction does not provide reasonable estimate . A visual selection of ICA components  still performed sub-optimally. On the other hand, multi-class implementation of CSP is also plagued with number of issues. It works on heuristics by employing one-vs-rest scheme . The resultant 4 projection matrices (in the case of 4-class problem) creates identification problem: there is no way to determine whether the observed differences in conditions are due to multiple projection matrices or distinct brain activity. No wonder, low detection rate is usually observed for unseen data.
Based on the physiological considerations left and right hand movement can be envisioned as (-vely) correlated tasks. That is, a trial associated with left imagination would typically depict de-synchronization over contra-lateral region (around C4) followed by synchronization over ipsi-lateral region (around C3). Almost diametrically reverse phenomenon is observed in the case of right movement imagination. Same line of argument is valid for foot and tongue movement imaginations . Therefore, one pair of correlated signals was separated from the other pair into distinct clusters- each containing only 4 components - by employing CSP. On account of CSP criterion of maximization of kurtosis by Eigen value decomposition , these clusters are nothing but orthogonal independent components. However, orthogonality with respect to independence criterion is an unnecessary constraint. Therefore, the basis was further rotated by employing infomax principle  to achieve maximal independence. Experimental paradigm and details of feature extraction and classification scheme can be found in . Succinctly, analysis was performed on 4 class motor imagery data sets consisted of 8 subjects and two sessions. Spatial filters and classifiers were built on the first session and employed on second session. For estimating ICA and ISP data sets were pre-filtered with same settings. Evaluation and estimation of CSP is detailed in .
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