Classification of left and right hand motor imagery EEG signals by using deep neural networks

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Korhan N. , Abilzade L., Ölmez T. , Ölmez Z.

International Journal of Applied Mathematics Electronics and Computers, vol.9, no.4, pp.85-90, 2021 (Refereed Journals of Other Institutions)

  • Publication Type: Article / Article
  • Volume: 9 Issue: 4
  • Publication Date: 2021
  • Title of Journal : International Journal of Applied Mathematics Electronics and Computers
  • Page Numbers: pp.85-90


The brain-computer interface (BCI) is one of the most promising technologies that allows us to establish a relationship between brain and devices. In this study, three-channel EEG signals collected from nine subjects performing two motor imagery tasks are classified using two different deep neural network (DNN) based approaches called framework 1 (FW1) and framework 2 (FW2). The proposed frameworks were evaluated using BCI Competition IV-IIb dataset. In FW1, the raw EEG data is directly presented to the deep neural network without performing any pre-processing. In FW2, the EEG data is first filtered with five band pass filters with fifth order (Butterworth), then the common spatial patterns (CSP) method, which introduces additional pseudo channels, is applied to the filtered signals. Two experiments were conducted for each framework. In the first experiment, a unique DNN is trained for each subject, and in the second experiment only one DNN is trained with the combination of training sets of all subjects. The performance of the two experiments are then compared in terms of average accuracy. According to the simulation results, we did not observe a significant difference between the average classification accuracies obtained with the first and the second experiments. Therefore, we concluded that, by the use of DNNs we do not need to train several subject-specific networks which requires high computational loads. On the other hand, we observed that the average classification performance significantly improves by the filtering and extracting features with CSP pre-processes.