Abstract
To increase the performance Motor imagery (MI) EEG classification, a multi-scale window length algorithm was proposed in this paper. Under this algorithm, eight different time windows with the length of 600–2000 ms were selected to classify the MI data. The confidence level of the classification results from the eight windows were calculated simultaneously, to determine the weights for the outputs of each pre-classifier. The final classification results of MI tasks were generated by the weighted results from the eight windows. Using the proposed algorithm, the response time of MI classification was decreased by 11.4%, comparing to the single window method. On the same level of response time, the classification accuracy was increased by 2.8% by the multi-scale window algorithm. These results show that the proposed algorithm can speed up the detection of MI tasks with reliable classification accuracy.
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Acknowledgement
This research was supported in part by National Natural Science Foundation of China under Grant 61375117 and 9142030002; National Program on Key Basic Research Project 2015CB351706.
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Jiang, J., Zhao, B., Zhang, P., Yu, Y. (2017). Motor Imagery EEG Classification Based on Multi-scale Time Windows. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_38
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DOI: https://doi.org/10.1007/978-3-319-67777-4_38
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