Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing
| العنوان: | Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing |
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| المؤلفون: | Andrzej Cichocki, Jing Jin, Shurui Li, Yangyang Miao, Ian Daly, Chang Liu, Xingyu Wang |
| المصدر: | IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28:2153-2163 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Wilcoxon signed-rank test, Computer science, Biomedical Engineering, 02 engineering and technology, Electroencephalography, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, 0202 electrical engineering, electronic engineering, information engineering, Internal Medicine, medicine, Humans, Brain–computer interface, medicine.diagnostic_test, Computers, business.industry, General Neuroscience, Rehabilitation, Brain, Signal Processing, Computer-Assisted, Pattern recognition, Brain-Computer Interfaces, Frequency domain, Imagination, 020201 artificial intelligence & image processing, Noise (video), Artificial intelligence, business, Bispectrum, 030217 neurology & neurosurgery, Communication channel |
| الوصف: | The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05). |
| تدمد: | 1558-0210 1534-4320 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::274320692c39f6cea31ad51e6ead97d4Test https://doi.org/10.1109/tnsre.2020.3020975Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....274320692c39f6cea31ad51e6ead97d4 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580210 15344320 |
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