Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 11; pp. 5619 - 5629
• Main Authors: Sakhavi, Siavash, Guan, Cuntai, Yan, Shuicheng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biological neural networks; Brain; Brain–computer interface (BCI); Classification; Computer applications; Computer architecture; Computer vision; Convolution; convolutional neural network (CNN); Data processing; deep learning (DL); EEG; Electroencephalography; Feature extraction; Filter banks; Heuristic algorithms; Human-computer interface; Image classification; Implants; Interfaces; Machine learning; Mental task performance; motor imagery (MI); Natural language processing; Neural networks; Representations; signal processing; Task analysis
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2018.2789927

Deep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems. However, the successful application of these methods in motor imagery (MI) brain-computer interfaces (BCIs), in order to boost classification performance, is still limited. In this paper, we propose a classification framework for MI data by introducing a new temporal representation of the data and also utilizing a convolutional neural network (CNN) architecture for classification. The new representation is generated from modifying the filter-bank common spatial patterns method, and the CNN is designed and optimized accordingly for the representation. Our framework outperforms the best classification method in the literature on the BCI competition IV-2a 4-class MI data set by 7% increase in average subject accuracy. Furthermore, by studying the convolutional weights of the trained networks, we gain an insight into the temporal characteristics of EEG.