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10.1007/S00521-020-05670-4

In this paper, the deep multi-source adaptation transfer network (DMATN) is proposed for the new subjects in aBCI.

A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition

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10.1109/JBHI.2021.3080502

To address this, we propose a novel deep multi-source Unsupervised Domain Adaptation (UDA) approach, namely Maximum Cross-Domain Classifier Discrepancy (MCDCD), which aims to improve the classification performance on the test subject (target domain) by leveraging the information from multiple labelled training subjects (source domains).

MCDCD: Multi-Source Unsupervised Domain Adaptation for Abnormal Human Gait Detection

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What is/are Deep Multi Source?

Deep Multi Source - In this paper, the deep multi-source adaptation transfer network (DMATN) is proposed for the new subjects in aBCI. [1] To address this, we propose a novel deep multi-source Unsupervised Domain Adaptation (UDA) approach, namely Maximum Cross-Domain Classifier Discrepancy (MCDCD), which aims to improve the classification performance on the test subject (target domain) by leveraging the information from multiple labelled training subjects (source domains). [2]