Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition
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, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. 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 T-test(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.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGElectroencephalogram (EEG)Emotion RecognitionSimilar Papers 제목 키워드 기반
Distributed $H_{\infty}$ Edge Weight Synthesis for Cooperative Systems
This paper studies distributed edge weight synthesis of a cooperative system for a fixed topology to improve $H_{\infty}$ performance, considering that disturbances are injected at interconnection channels. This problem …
On the use of higher-order tensors to model muscle synergies
The muscle synergy concept provides the best framework to understand motor control and it has been recently utilised in many applications such as prosthesis control. The current muscle synergy model relies on decomposing…
Electromyography (EMG)Tensor DecompositionDual Reweighted Lp-Norm Minimization for Salt-and-pepper Noise Removal
The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision. It is usually replaced …
Salt-And-Pepper Noise RemovalSite-specific online compressive beam codebook learning in mmWave vehicular communication
Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility in vehicular communication make it chal…
Compressive SensingSubgraph Detection Using Eigenvector L1 Norms
When working with network datasets, the theoretical framework of detection theory for Euclidean vector spaces no longer applies. Nevertheless, it is desirable to determine the detectability of small, anomalous graphs emb…