Abstract
Collecting sufficient labeled electroencephalography (EEG) data to build an individual classifier for each subject is extremely time-consuming and labor-intensive, especially for the disabled patients. A feasible way is to use labeled EEG data from other subjects (source domains) to train a model for classifying EEG data from the new subjects (target domains). However, the model trained using other subjects EEG data may degrade the classification performance of the target subject, when there exists the substantial inter-subject variability of EEG data. In this paper, to account for the domain shift between different subjects, we propose a novel deep domain adaptation network (DDAN) for cross-subject EEG signal recognition. Specifically, a special end-to-end convolutional neural network (CNN) is firstly adopted to automatically extract deep features from the raw EEG data. Then, maximum mean discrepancy (MMD) is used to minimize the distribution discrepancy of deep features between source and target subjects. Finally, a center-based discriminative feature learning (CDFL) method is used to force the deep features closer to their corresponding class centers and make the inter-class centers more separable, so that it is possible to further improve the recognition performance of target domain EEG data. Experiments on public EEG datasets prove the effectiveness of the proposed method. This study may promote the practical use of EEG signal processing technology and expand its application range.
Highlights
Among various measurements of brain cognitive activity, electroencephalography (EEG) is widely used to record the electrical activity of the human brain due to its characteristic of non-invasive, simplicity and high temporal resolution [1]
We propose a novel deep domain adaptation network (DDAN) for cross-subject EEG signal recognition, which simultaneously uses specific deep neural network based on the intrinsic characteristics of EEG data to automatically extract their features, and learning more transferable and discriminative EEG features to eliminate the domain shift between different subjects
2) STATISTICAL SIGNIFICANCE TESTING We further investigate whether the improvement in classification performances of the proposed DDAN is at a significant level
Summary
Among various measurements of brain cognitive activity, electroencephalography (EEG) is widely used to record the electrical activity of the human brain due to its characteristic of non-invasive, simplicity and high temporal resolution [1]. Once a specific classifier is picked up, how to efficiently determine the optimal classifier parameters remains a challenge These shallow domain adaptation methods applied in EEG signal recognition predominantly utilize the human-designed feature to match different subject distributions, which require considerable field experience. We propose a novel deep domain adaptation network (DDAN) for cross-subject EEG signal recognition, which simultaneously uses specific deep neural network based on the intrinsic characteristics of EEG data to automatically extract their features, and learning more transferable and discriminative EEG features to eliminate the domain shift between different subjects. The contributions of this paper are summarized as follows: 1) We propose a novel deep neural network called DDAN for the cross-subject EEG signal classification instead of the traditional domain adaptation methods.
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