A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface. (6th January 2022)

• Record Type: Journal Article
• Authors: Mattioli, F; Porcaro, C; Baldassarre, G
• Is Part Of: Journal of neural engineering. Volume 18:Number 6(2021)
• Publication Date: 2022-01-06
• Subjects: brain-computer interface -- BCI -- electroencephalogram -- deep neural networks -- data augmentation -- transfer learning; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac4430
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach. We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its late layers with only 12-min individual-related data. Main results. The model tested with the 'EEG Motor Movement/Imagery Dataset' outperforms the current state-of-the-art models by achieving a 99.38 % accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of 99.46 % . Significance. The proposed methods could foster the development of future BCI applications relying on few-channel portable recording devices and individual-based training.