Deep comparisons of Neural Networks from the EEGNet family

TL;DR

This study systematically compares five neural network architectures for EEG-based Motor Imagery classification across multiple datasets, incorporating artifact removal and transfer learning to evaluate their effectiveness and generalization.

Contribution

It introduces a comprehensive comparison framework with new metrics, demonstrating that older architectures like Shallow and Deep ConvNet outperform some newer EEGNet variants.

Findings

Shallow ConvNet and Deep ConvNet outperform EEGNet variants in accuracy.

Artifact removal with FASTER improves classification results.

Transfer learning enhances neural network performance on EEG data.

Abstract

Most of the Brain-Computer Interface (BCI) publications, which propose artificial neural networks for Motor Imagery (MI) Electroencephalography (EEG) signal classification, are presented using one of the BCI Competition datasets. However, these databases contain MI EEG data from less than or equal to 10 subjects . In addition, these algorithms usually include only bandpass filtering to reduce noise and increase signal quality. In this article, we compared 5 well-known neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, MI-EEGNet) using open-access databases with many subjects next to the BCI Competition 4 2a dataset to acquire statistically significant results. We removed artifacts from the EEG using the FASTER algorithm as a signal processing step. Moreover, we investigated whether transfer learning can further improve the classification results on artifact filtered data. We aimed to rank the neural networks; therefore, next to the classification accuracy, we introduced two additional metrics: the accuracy improvement from chance level and the effect of transfer learning. The former can be used with different class-numbered databases, while the latter can highlight neural networks with sufficient generalization abilities. Our metrics showed that the researchers should not avoid Shallow ConvNet and Deep ConvNet because they can perform better than the later published ones from the EEGNet family.