One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.
Cite this article
Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces
1 School of Electronic and Information Engineering (Department of Physics), Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong Province, 250353, P. R. China
2 School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong Province, 250353, P. R. China
3 Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong Province, 250358, P. R. China
4 School of Microelectronics, Shandong University, Jinan, Shandong Province, 250100, P. R. China
*Correspondence: email@example.com (Fangzhou Xu)
J. Integr. Neurosci. 2020, 19(2), 259–272; https://doi.org/10.31083/j.jin.2020.02.1269
Submitted: 25 December 2019 | Revised: 8 May 2020 | Accepted: 21 May 2020 | Published: 30 June 2020
Copyright: © 2020 Xu et al. Published by IMR press.
This is an open access article under the CC BY-NC 4.0 license
optimized wrapper approach