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Open AccessArticle

Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding

by Xingliang Tang 1,2,* and Xianrui Zhang 3,*
1
School of Information Science and Engineering, LanZhou University, Lanzhou 730000, China
2
Sichuan Jiuzhou Electric Group Co Ltd, Mianyang 621000, China
3
Department of Automation Sciences, Beihang University, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Entropy 2020, 22(1), 96; https://doi.org/10.3390/e22010096
Received: 5 December 2019 / Revised: 1 January 2020 / Accepted: 10 January 2020 / Published: 13 January 2020
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems II)
Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features from raw EEG signals automatically. However, the challenge that the deep learning method faces is that the shortage of labeled EEG signals and EEGs sampled from other subjects cannot be used directly to train a convolutional neural network (ConvNet) for a target subject. To solve this problem, in this paper, we present a novel conditional domain adaptation neural network (CDAN) framework for MI EEG signal decoding. Specifically, in the CDAN, a densely connected ConvNet is firstly applied to obtain high-level discriminative features from raw EEG time series. Then, a novel conditional domain discriminator is introduced to work as an adversarial with the label classifier to learn commonly shared intra-subjects EEG features. As a result, the CDAN model trained with sufficient EEG signals from other subjects can be used to classify the signals from the target subject efficiently. Competitive experimental results on a public EEG dataset (High Gamma Dataset) against the state-of-the-art methods demonstrate the efficacy of the proposed framework in recognizing MI EEG signals, indicating its effectiveness in automatic perceptual decision decoding.
Keywords: electroencephalogram (EEG); motor imagery (MI); domain adaptation; signal classification; convolutional neural network electroencephalogram (EEG); motor imagery (MI); domain adaptation; signal classification; convolutional neural network
MDPI and ACS Style

Tang, X.; Zhang, X. Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding. Entropy 2020, 22, 96.

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