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

Wang, Fei; Zhang, Weiwei; Xu, Zongfeng; Ping, Jingyu; Chu, Hao

doi:10.1007/s00521-020-05670-4

Cited by 27 publications

(4 citation statements)

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“…By simultaneously optimizing the loss functions of the MK-MMDs and the task, the DAN can reduce domain shift across domains, meanwhile preserving domain-invariant and task-related features. A multi-source adaptation transfer network (DMATN) for cross-subject emotion recognition is proposed by Wang et al [190], as shown in Figure 26(B). The mechanism of this model is exactly similar to the model proposed by Li et al [193].…”

Section: Experiments Detailsmentioning

confidence: 99%

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EEG Based Emotion Recognition: A Tutorial and Review

et al. 2022

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Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions.

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Section: Experiments Detailsmentioning

confidence: 99%

“…A: The Wasserstein generative adversarial network based domain adaptation approach 13[189]. B: The multi-source adaptation transfer network (DMATN) based domain adaptation approach that integrates the deep adversarial network and the multi-kernel maximum mean discrepancies (MK-MMDs) measurement 14[190].…”

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confidence: 99%

EEG Based Emotion Recognition: A Tutorial and Review

et al. 2022

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“…Unfortunately, these traditional methods may fail in addressing cross-subject/dataset emotion recognition due to the mismatch of feature distribution with EEG signals. To address this issue, many domain adaptation (DA) emotion recognition models for AER problem have been promoted ( Chu et al, 2017 ; Li et al, 2018a ; Li et al, 2020a , b ; Bao et al, 2021 ; Wang et al, 2021 ). In a DA emotion recognition system, one usually focuses on exploring an effective recognition model on one target domain with few or even none of the labeled data, by borrowing some positive knowledge from other source domain(s) with slightly different distribution with that of the target domain ( Bruzzone and Marconcini, 2010 ; Tao et al, 2012 ; Long et al, 2014 ; Zhang et al, 2019b ).…”

Section: Introductionmentioning

confidence: 99%

Robust Latent Multi-Source Adaptation for Encephalogram-Based Emotion Recognition

et al. 2022

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In practical encephalogram (EEG)-based machine learning, different subjects can be represented by many different EEG patterns, which would, in some extent, degrade the performance of extant subject-independent classifiers obtained from cross-subjects datasets. To this end, in this paper, we present a robust Latent Multi-source Adaptation (LMA) framework for cross-subject/dataset emotion recognition with EEG signals by uncovering multiple domain-invariant latent subspaces. Specifically, by jointly aligning the statistical and semantic distribution discrepancies between each source and target pair, multiple domain-invariant classifiers can be trained collaboratively in a unified framework. This framework can fully utilize the correlated knowledge among multiple sources with a novel low-rank regularization term. Comprehensive experiments on DEAP and SEED datasets demonstrate the superior or comparable performance of LMA with the state of the art in the EEG-based emotion recognition.

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“…After private component extraction for unknown subjects, the more similar subject private classifiers would be granted higher weights, which would influence the joint classification of private and shared classifiers, and the result of 86.7 ± 7.1% was obtained on the SEED dataset [18]. In general, the main solutions of domain differences caused by individual information are transfer learning [7], [19]- [21] and deep learning [10], [15], [22]. For the results, both methods can obtain excellent results, but both miss the analysis of EEG information and the interpretability of EEG individual differences, which as the basic theory of affective brain-computer interfaces is more important for EEG-based emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Weight-based Channel-model Matrix Framework: a reasonable solution for EEG-based cross-dataset emotion recognition

Chen¹,

He²,

Sun³

et al. 2022

Preprint

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Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, we found the Individual Emotional Feature Distribution Difference(IEFDD). After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction Ttest(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability.

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

Cited by 27 publications

References 20 publications

EEG Based Emotion Recognition: A Tutorial and Review

EEG Based Emotion Recognition: A Tutorial and Review

Robust Latent Multi-Source Adaptation for Encephalogram-Based Emotion Recognition

Weight-based Channel-model Matrix Framework: a reasonable solution for EEG-based cross-dataset emotion recognition

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