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Mobarezpour, Jahangir, Khosrowabadi, Reza, Ghaderi, Reza, Navi, Keivan. (1398). Classification of EEG-based motor imagery BCI by using ECOC. نشریه هنرهای کاربردی, 10(2), 23-33. doi: 10.22075/ijnaa.2019.17634.1955
Jahangir Mobarezpour; Reza Khosrowabadi; Reza Ghaderi; Keivan Navi. "Classification of EEG-based motor imagery BCI by using ECOC". نشریه هنرهای کاربردی, 10, 2, 1398, 23-33. doi: 10.22075/ijnaa.2019.17634.1955
Mobarezpour, Jahangir, Khosrowabadi, Reza, Ghaderi, Reza, Navi, Keivan. (1398). 'Classification of EEG-based motor imagery BCI by using ECOC', نشریه هنرهای کاربردی, 10(2), pp. 23-33. doi: 10.22075/ijnaa.2019.17634.1955
Mobarezpour, Jahangir, Khosrowabadi, Reza, Ghaderi, Reza, Navi, Keivan. Classification of EEG-based motor imagery BCI by using ECOC. نشریه هنرهای کاربردی, 1398; 10(2): 23-33. doi: 10.22075/ijnaa.2019.17634.1955

Classification of EEG-based motor imagery BCI by using ECOC

International Journal of Nonlinear Analysis and Applications
مقاله 36، دوره 10، شماره 2، اسفند 2019، صفحه 23-33    اصل مقاله (211.67 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2019.17634.1955
نویسندگان
Jahangir Mobarezpour1؛ Reza Khosrowabadi* 2؛ Reza Ghaderi3؛ Keivan Navi3
1Shahid Beheshti University Institute for Cognitive and Brain Sciences
2Shahid Beheshti University Institute for Cognitive and Brain ScienceS
3Shahid Beheshti University Institute for Cognitive and Brain Sciences
تاریخ دریافت: 27 خرداد 1397،  تاریخ بازنگری: 10 تیر 1398،  تاریخ پذیرش: 20 شهریور 1398 
چکیده
Accuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as various feature extraction methods, learning algorithms, and classifier schemes have been developed in this regard. However, conducting more research is necessary for improvement. The present study aimed to use an ensemble learning approach to improve the performance of MI-BCI systems. Therefore, filter bank common spatial pattern (FBCSP), as a well-known feature extraction method, was used to produce separable features from EEG signals. Accordingly, error correcting output codes (ECOC) was applied on several learning algorithms to classify four classes of motor imagery tasks. The proposed ECOC ensemble technique was tested on the data set 2a from BCI competition IV. Based on the results, the ECOC can lead to an improvement by using the naive Bayesian parzen window algorithm, compared to the winner algorithm of BCI competition IV, which is superior to other selected state of the art algorithms.
کلیدواژه‌ها
Keywords: Brain computer interface (BCI)؛ Error Correcting Output Codes (ECOC)؛ Electroencephalography (EEG)؛ Motor imagery؛ Filter bank common spatial pattern (FBCSP)
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