CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the State-of-The-Art to DynamicNet

The accurate detection of motor imagery (MI) from electroencephalography (EEG) is a fundamental, as well as challenging, task to provide reliable control of robotic devices to support people suffering from neuro-motor impairments, e.g., in brain-computer interface (BCI) applications. Recently, deep learning approaches have been able to extract subject-independent features from EEG, to cope with its poor SNR and high intra-subject and cross-subject variability. In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance. Second, we propose DynamicNet, a Python-based tool for quick and flexible implementations of deep learning models based on convolutional neural networks. We showcase the potentiality of DynamicNet by implementing EEGNet, a well-established architecture for effective EEG classification. Finally, we compare its performance with the filter bank common spatial pattern (FBCSP) in a 4-class MI task (data from a public dataset). To infer cross-subject classification performance, we applied three different cross-validation schemes. From our results, we show that EEGNet implemented with DynamicNet outperforms FBCSP by about 25%, with a statistically significant difference when cross-subject validation schemes are applied. We conclude that deep learning approaches might be particularly helpful to provide higher cross-subject classification performance in multi-class MI classification scenarios. In the future, it is expected to improve DynamicNet to implement new architectures to further investigate cross-subject classification of MI tasks in real-world scenarios.

Automated summary

This paper reviews recent studies on using deep learning for MI classification, introduces the DynamicNet tool for EEG classification, and demonstrates the superiority of deep learning approaches over traditional methods when applied to cross-subject classification tasks.