Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks
- Authors
- Kwon, O-Yeon; Lee, Min-Ho; Guan, Cuntai; Lee, Seong-Whan
- Issue Date
- Oct-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Electroencephalography; Databases; Feature extraction; Electrodes; Brain modeling; Task analysis; Calibration; Brain-computer interface (BCI); convolutional neural networks (CNNs); deep learning (DL); electroencephalography (EEG); motor imagery (MI); subject-independent
- Citation
- IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.31, no.10, pp.3839 - 3852
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
- Volume
- 31
- Number
- 10
- Start Page
- 3839
- End Page
- 3852
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/52607
- DOI
- 10.1109/TNNLS.2019.2946869
- ISSN
- 2162-237X
- Abstract
- For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20-30 min to collect enough data to build a reliable decoder. It is, therefore, an interesting topic to build a calibration-free, or subject-independent, BCI. In this article, we construct a large motor imagery (MI)-based electroencephalography (EEG) database and propose a subject-independent framework based on deep convolutional neural networks (CNNs). The database is composed of 54 subjects performing the left- and right-hand MI on two different days, resulting in 21 600 trials for the MI task. In our framework, we formulated the discriminative feature representation as a combination of the spectral-spatial input embedding the diversity of the EEG signals, as well as a feature representation learned from the CNN through a fusion technique that integrates a variety of discriminative brain signal patterns. To generate spectral-spatial inputs, we first consider the discriminative frequency bands in an information-theoretic observation model that measures the power of the features in two classes. From discriminative frequency bands, spectral-spatial inputs that include the unique characteristics of brain signal patterns are generated and then transformed into a covariance matrix as the input to the CNN. In the process of feature representations, spectral-spatial inputs are individually trained through the CNN and then combined by a concatenation fusion technique. In this article, we demonstrate that the classification accuracy of our subject-independent (or calibration-free) model outperforms that of subject-dependent models using various methods [common spatial pattern (CSP), common spatiospectral pattern (CSSP), filter bank CSP (FBCSP), and Bayesian spatio-spectral filter optimization (BSSFO)].
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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