Mutual information-based selection of optimal spatial–temporal patterns for single-trial EEG-based BCIs
| Autor: | Zheng Yang Chin, Haihong Zhang, Cuntai Guan, Kai Keng Ang |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
medicine.diagnostic_test
Computer science Frequency band business.industry Pattern recognition Feature selection Mutual information Electroencephalography Naive Bayes classifier Artificial Intelligence Signal Processing medicine Common spatial pattern Computer Vision and Pattern Recognition Artificial intelligence business Software Decoding methods Selection (genetic algorithm) |
| Zdroj: | Pattern Recognition. 45:2137-2144 |
| ISSN: | 0031-3203 |
| DOI: | 10.1016/j.patcog.2011.04.018 |
| Popis: | The common spatial pattern (CSP) algorithm is effective in decoding the spatial patterns of the corresponding neuronal activities from electroencephalogram (EEG) signal patterns in brain-computer interfaces (BCIs). However, its effectiveness depends on the subject-specific time segment relative to the visual cue and on the temporal frequency band that is often selected manually or heuristically. This paper presents a novel statistical method to automatically select the optimal subject-specific time segment and temporal frequency band based on the mutual information between the spatial-temporal patterns from the EEG signals and the corresponding neuronal activities. The proposed method comprises four progressive stages: multi-time segment and temporal frequency band-pass filtering, CSP spatial filtering, mutual information-based feature selection and naive Bayesian classification. The proposed mutual information-based selection of optimal spatial-temporal patterns and its one-versus-rest multi-class extension were evaluated on single-trial EEG from the BCI Competition IV Datasets IIb and IIa respectively. The results showed that the proposed method yielded relatively better session-to-session classification results compared against the best submission. |
| Databáze: | OpenAIRE |
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