CNN models for EEG motor imagery signal classification

Motor imagery (MI) electroencephalography (EEG) signal classification plays an important role in brain-computer interface (BCI), which gives hope to amputees and disabled people.This paper proposes a number of convolutional neural networks (CNNs) models for EEG MI signal classification, and it also proposes a method for enhancing the classification accuracy by feeding the CNN model with different frequency bands filters, which shows a significant improvement. The proposed models are: (1) Basic model, which is the simplest model with only one layer and no convolution layers. (2) CNN1D where the convolution is carried out along time. (3) CNN2D, where time and sensor channels are presented as 2D signals and convolution is carried out along both of them. (4,5) CNN3D and TimeDist, where the EEG signals are represented as video signals. (6) CNN1D_MF, where MF stands for multiband frequency, which is a modified version of the CNN1D model with multiple frequecy bands input. Physionet (5 classes) and BCI Competition IV-2a (4 classes) datasets were used in the evaluation. The software used for this paper, namely Coleeg, works on Google (TM) Colaboratory and Python language. The validation accuracy, validation kappa, and time were measured and compared to three existing models, which are: E1F,GNet, ShallowConvNet, and DeepConvNet. The results show that CNN1D_MF model has the best accuracy results, with 58.0% and 69.2% for Physionet and BCI Competition IV-2a datasets, respectively. Coleeg code is available under an open-source license to be an initiative for collaboration in EEG signal classification.

Automated summary

This paper proposes several CNN models for EEG MI signal classification and suggests a method to improve classification accuracy by using different frequency bands filters. The results show that the CNN1D_MF model achieves the highest accuracy on the evaluated datasets.