Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets

By Zirui Lan, Olga Sourina, Lipo Wang, Reinhold Scherer, Gernot R. Müller-Putz. IEEE Transactions on Cognitive and Developmental Systems, 2019

321 citations recorded by the source corpus.

Affective brain–computer interface (aBCI) introduces personal affective factors to human–computer interaction. The state-of-the-art aBCI tailors its classifier to each individual user to achieve accurate emotion classification. A subject-independent classifier that is trained on pooled data from multiple subjects generally leads to inferior accuracy, due to the fact that electroencephalography patterns vary from subject to subject. Transfer learning or domain adaptation techniques have been leveraged to tackle this problem. Existing studies have reported successful applications of domain adaptation techniques on SEED dataset. However, little is known about the effectiveness of the domain adaptation techniques on other affective datasets or in a cross-dataset application. In this paper, we focus on a comparative study on several state-of-the-art domain adaptation techniques on two datasets: 1) DEAP and 2) SEED. We demonstrate that domain adaptation techniques can improve the classification accuracy on both datasets, but not so effective on DEAP as on SEED. Then, we explore the efficacy of domain adaptation in a cross-dataset setting when the data are collected under different environments using different devices and experimental protocols. Here, we propose to apply domain adaptation to reduce the intersubject variance as well as technical discrepancies between datasets, and then train a subject-independent classifier on one dataset and test on the other. Experiment results show that using domain adaptation technique in a transductive adaptation setting can improve the accuracy significantly by 7.25%–13.40% compared to the baseline accuracy where no domain adaptation technique is used.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/6778368df92310ff447365f2145d50282334e93f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Bin Hu
2. Chunyan Miao
3. Prayag Tiwari
4. Yazhou Zhang
5. Meihong Yang
6. Di Wang
7. Xiang Li
8. Zhigang Zhao

In Experimental and Cognitive Psychology

1. Bin Hu
2. Chunyan Miao
3. Prayag Tiwari
4. Yazhou Zhang
5. Meihong Yang
6. Di Wang
7. Xiang Li
8. Zhigang Zhao

Citations in the corpus

• Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, unknown): “[103] considered cross-dataset transfers between DEAP and SEED, which have different numbers of subjects, and were recorded using different EEG devices with different numbers of electrodes.”
• EEG Based Emotion Recognition: A Tutorial and Review (2022, methodology): “[188] DEAP, SEED transfer learningmethods (MIDA,TCA) combines with differential entropy feature and logistic regression classifier 3 classes, subject-independent modeling (DEAP-MIDA: 0.”
• EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “Lan et al. [15] compared several domain adaptation techniques such as maximum independence domain adaptation (MIDA), transfer component analysis (TCA), sub-space alignment (SA), etc.”