MDDD: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition

• URL: http://arxiv.org/abs/2404.15615v1
• Date: Wed, 24 Apr 2024 03:08:25 GMT
• Title: MDDD: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition
• Authors: Ting Luo, Jing Zhang, Yingwei Qiu, Li Zhang, Yaohua Hu, Zhuliang Yu, Zhen Liang,
• Abstract summary: We propose a novel non-deep transfer learning method, termed as Manifold-based Domain adaptation with Dynamic Distribution (MDDD) The experimental results indicate that MDDD outperforms traditional non-deep learning methods, achieving an average improvement of 3.54%. This suggests that MDDD could be a promising method for enhancing the utility and applicability of aBCIs in real-world scenarios.
• Score: 11.252832459891566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces represents a significant area within the field of affective computing. In the present study, we propose a novel non-deep transfer learning method, termed as Manifold-based Domain adaptation with Dynamic Distribution (MDDD). The proposed MDDD includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The experimental results indicate that MDDD outperforms traditional non-deep learning methods, achieving an average improvement of 3.54%, and is comparable to deep learning methods. This suggests that MDDD could be a promising method for enhancing the utility and applicability of aBCIs in real-world scenarios.

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