A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

Abstract

Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for convolutional neural network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method’s promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods.

Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks

“…For both datasets, one session was used for training and the other was used for testing. Global hyperparameters, which were obtained in our previous work [ 40 ], were employed for all subjects, as shown in Table 1 . The learning rate was 0.0009, batch size was 64, and the number of epochs was 1000.…”

“…Table 2 shows the performance comparison between the proposed models and other SOTA models. In particular, the average classification accuracies, Kappa values, and F1 scores obtained by the FBCSP [ 25 ], ShallowConvNet [ 18 ], DeepConvNet [ 18 ], EEGNet [ 27 ], CP-MixedNet [ 34 ], TS-SEFFNet [ 37 ], MBEEGNet [ 40 ], and MBShallowCovNet [ 40 ] from the BCI-IV2a and HGD datasets are summarized in Table 2 . Our methods have the highest average accuracy, Kappa, and F1 score as can be observed.…”

“…Our methods have the highest average accuracy, Kappa, and F1 score as can be observed. Moreover, we compared our results with those of our previous work [ 40 ] which contains lightweight multi-branch models without attention blocks. We found that the attention block improves the accuracy by around 1%.…”

“… Average classification accuracy on the BCI-IV2a dataset. The methods compared are ShallowConvNet [ 18 ], DeepConvNet [ 18 ], FBCSP [ 25 ], EEGNet [ 27 ], CP-MixedNet [ 34 ], TS-SEFFNet [ 37 ], MBEEGNet [ 40 ], MBShallowConvNet [ 40 ], the proposed MBEEGCBAM, and the proposed FMBEEGCBAM. …”

“…In our previous work [ 40 ], we examine the multi-branch CNN in classifying the raw EEG-MI signal with fewer parameters. In this study, we investigate the effect of adding attention blocks to the multi-branch EEGNet.…”