TY - JOUR
T1 - Multisource Associate Domain Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition
AU - She, Qingshan
AU - Zhang, Chenqi
AU - Fang, Feng
AU - Ma, Yuliang
AU - Zhang, Yingchun
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2023
Y1 - 2023
N2 - Emotion recognition is important in the application of brain-computer interface (BCI). Building a robust emotion recognition model across subjects and sessions is critical in emotion-based BCI systems. Electroencephalogram (EEG) is a widely used tool to recognize different emotion states. However, EEG has disadvantages such as small amplitude, low signal-to-noise ratio, and nonstationary properties, resulting in large differences across subjects. To solve these problems, this article proposes a new emotion recognition method based on a multisource associate domain adaptation (DA) network, considering both domain invariant and domain-specific features. First, separate branches were constructed for multiple source domains, assuming that different EEG data shared the same low-level features. Second, the domain-specific features were extracted using the one-to-one associate DA. Then, the weighted scores of specific sources were obtained according to the distribution distance, and multiple source classifiers were deduced with the corresponding weighted scores. Finally, EEG emotion recognition experiments were conducted on different datasets, including SEED, DEAP, and SEED-IV dataset. Results indicated that, in the cross-subject experiment, the average accuracy in SEED dataset was 86.16%, DEAP dataset was 65.59%, and SEED-IV was 59.29%. In the cross-session experiment, the accuracies of SEED and SEED-IV datasets were 91.10% and 66.68%, respectively. Our proposed method has achieved better classification results compared to the state-of-the-art DA methods.
AB - Emotion recognition is important in the application of brain-computer interface (BCI). Building a robust emotion recognition model across subjects and sessions is critical in emotion-based BCI systems. Electroencephalogram (EEG) is a widely used tool to recognize different emotion states. However, EEG has disadvantages such as small amplitude, low signal-to-noise ratio, and nonstationary properties, resulting in large differences across subjects. To solve these problems, this article proposes a new emotion recognition method based on a multisource associate domain adaptation (DA) network, considering both domain invariant and domain-specific features. First, separate branches were constructed for multiple source domains, assuming that different EEG data shared the same low-level features. Second, the domain-specific features were extracted using the one-to-one associate DA. Then, the weighted scores of specific sources were obtained according to the distribution distance, and multiple source classifiers were deduced with the corresponding weighted scores. Finally, EEG emotion recognition experiments were conducted on different datasets, including SEED, DEAP, and SEED-IV dataset. Results indicated that, in the cross-subject experiment, the average accuracy in SEED dataset was 86.16%, DEAP dataset was 65.59%, and SEED-IV was 59.29%. In the cross-session experiment, the accuracies of SEED and SEED-IV datasets were 91.10% and 66.68%, respectively. Our proposed method has achieved better classification results compared to the state-of-the-art DA methods.
KW - Domain adaptation (DA)
KW - electroencephalogram (EEG)
KW - emotion recognition
KW - transfer learning
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U2 - 10.1109/TIM.2023.3277985
DO - 10.1109/TIM.2023.3277985
M3 - Article
AN - SCOPUS:85160243573
SN - 0018-9456
VL - 72
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 2515512
ER -