Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity

Multimodal Emotion Recognition Using a Hierarchical Fusion Convolutional Neural Network

“…Since human beings can rely on their active consciousness to cover their behaviors and existing technologies cannot elicit the true emotional state of human beings, behavior-based emotion recognition has certain limitations. Therefore, research on the use of physiological signals to recognize emotions has received increasingly more attention [16]. In view of the different information carried by the signals of different modes, in order to make an emotion recognition system more accurate, the fusion of the signals from multiple modalities to recognize emotions has attracted the interest of an increasing number of researchers.…”

“…Taking into account that the brain regions related to the frontal lobe have high recognition accuracy [28], the 6-channel EEG signals of the forehead and the PPS signals of other remaining channels are used as experimental data in the experiment. The data is downsampled to 128 Hz, and five bands including the delta (4-8 Hz), theta (8-13 Hz), alpha (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), beta (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43), and gamma bands (4-43 Hz) are filtered out. Due to the error in the first 3 s of the video in the experiment, the first 3 s of the video are removed, and the middle 30 s of the remaining duration of the video are used as experimental data.…”

Low-Dimensional Subject Representation-Based Transfer Learning in EEG Decoding

“…This section reviews the related works on transfer learning techniques for EEG-based BCI. Although some research aimed Transfer Learning for BCI Targeted Subject [10,19,20,21,22] Calibration Data [12,23,24,25,26,27] [ 28,11,29,30,31,32] [ 5,9], Proposed at intra-subject-cross session transfer [34], this study will mainly focus on the review of inter-subject transfer research. Fig.…”

“…First, the baseline-aligning-based transformation method aligns the features of data by removing subjectspecific baselines so that the model can be trained from all the aligned data [24,25,28]. The Distribution-matching-based transformation method aims to transform the features of the domain into a latent subspace, where the differences of the feature distributions are small [22,26,27,12,32]. Also, both [12] 2019 Distribution-matching Feature transformation by affine mapping Dagois et al [11] 2019 Instance Subject-transfer by similarity of data distribution Zhang et al [30] 2019 Instance Instance transfer by the similarity of the data distribution Zhang et al [29] 2019 Feature-representation Invariant feature recurrent attention network Jeon et al [31] 2019 Feature-representation Invariant feature by DNN with mutual information maximization Li et al [32] 2019 Distribution-matching Feature transformation by both marginal and conditional distribution matching Pre-trial/Resting Data Bolagh and Clifford [9] 2017 Instance Subject-transfer by Riemannian geometry Wei et al [5] 2018 Parameter Subject-transfer by multiple distance metrics Proposed 2020 Instance Subject-transfer based on subject representation approaches can be integrated together to reduce the differences [23].…”

Advances in Multimodal Emotion Recognition Based on Brain–Computer Interfaces

“… For the session–session problem, similar to the cross-subject problem, the previous session is the source domain, and the new session is the target domain. A novel domain adaptation method was proposed in [ 129 ] for EEG emotion recognition that showed superiority for both cross-session and cross-subject adaptation. It integrates task-invariant features and task-specific features in a unified framework and requires no labeled information in the target domain to accomplish joint distribution adaptation (JDA).…”