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Optimal Channel Selection Using Correlation Coefficient for CSP Based EEG Classification
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DOI：10.1109/ACCESS.2020.3003056.png)
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摘要
En 中文
In this paper, we present an optimal channel selection method to improve common spatial pattern (CSP) related features for motor imagery (MI) classification. In contrast to existing channel selection methods, in which channels significantly contributing to the classification in terms of the signal power are selected, distinctive channels in terms of correlation coefficient values are selected in the proposed method. The distinctiveness of a channel is quantified by the number of channels with which it yields large difference in correlation coefficient values for binary motor imagery (MI) tasks, rather than by the largeness of the difference itself. For each distinctive channel, a group of channels is formed by gathering strongly correlated channels and the Fisher score is computed using the feature output, based on the filter-bank CSP (FBCSP) exclusively applied to the channel group. Finally, the channel group with the highest Fisher score is chosen as the selected channels. The proposed method selects the fewest channels on average and outperforms existing channel selection approaches. The simulation results confirm performance improvement for two publicly available BCI datasets, BCI competition III dataset IVa and BCI competition IV dataset I, in comparison with existing methods.
Keyword:
Electroencephalography
Correlation
Task analysis
Feature extraction
Communications technology
Planning
Government
Electroencephalography (EEG)
brain-computer interfaces (BCIs)
correlation coefficient
common spatial pattern (CSP)
channel selection
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期刊
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3.6
论文数:
9.8W
被引数:
29.4W
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Correlation-based channel selection and regularized feature optimization for MI-based BCI.png)
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