Work

Multimodal Fusion of EEG and Musical Features in Music-Emotion Recognition

Year: 2017

Type: conference-paper

Abstract: Multimodality has been recently exploited to overcome the challenges of emotion recognition. In this paper, we present a study of fusion of electroencephalogram (EEG) features and musical features extracted from musical stimuli at decision level in recognizing the time-varying binary classes of arousal and valence. Our empirical results demonstrate that EEG modality was suffered from the non-stability of EEG signals, yet fusing with music modality could alleviate the issue and enhance the performance of emotion recognition.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v31i1.11112

Authors: Nattapong Thammasan, Ken-ichi Fukui, Masayuki Numao

Institution: The University of Osaka

Language: English

Topic: Emotion and Mood Recognition

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2604409433

FWCI: 0.991

Cites: 5

Cited by: 26

Related to: 10