AUTHOR=Wu Hao , Niu Yi , Li Fu , Li Yuchen , Fu Boxun , Shi Guangming , Dong Minghao 

TITLE=A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.01275

DOI=10.3389/fnins.2019.01275

ISSN=1662-453X

ABSTRACT=Objective. Electroencephalogram (EEG) based brain–computer interfaces (BCIs) in motor imagery (MI) have developed rapidly in recent years. A reliable feature extraction method is essential because of a low signal-to-noise ratio and time-dependent covariates of EEG signals. Because of efficient application in various fields, deep learning has been adopted in EEG signal processing and has obtained competitive results compared with the traditional methods. However, designing and training an end-to-end network to fully extract potential features from EEG signals remains a challenge in MI. Approach. In this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification. We introduce a layered end-to-end network structure, in which a feature-extraction network is used to extract temporal and spatial features. To enhance the transfer learning ability, we propose a network initialization and fine-tuning strategy to train an individual model for intersubject classification on small datasets. We compare our MSFBCNN with the state-of-the-art approaches on open datasets. Results. The proposed method has a higher accuracy than the baselines in intrasubject classification. In addition, the transfer learning experiments indicate that our network can build an individual model and obtain acceptable results in intersubject classification. The results suggest that the proposed network has superior performance, robustness, and transfer learning ability.