Work

EmoDLNet: End-to-End Multi-Scale Spatio-Temporal Deep Learning for EEG-Based Emotion Recognition in Affective Human-Computer Interaction

Year: 2026

Type: article

Abstract: Real-time emotion recognition from EEG signals is crucial for enhancing user engagement in human-computer interaction (HCI) applications, yet extracting emotion-specific features remains a challenge. This paper proposes EmoDLNet, a deep learning framework combining convolutional neural networks (CNNs) and hierarchical gated recurrent units (GRUs) to automate feature extraction from raw EEG data for emotion classification. We evaluate EmoDLNet on two datasets: DREAMER for binary arousal/valence classification and SEED-V for multi-class (happy, sad, fear, disgust, and neutral) emotion classification. On DREAMER, EmoDLNet achieved 89.4% (arousal) and 84.5% (valence) classification accuracy, surpassing state-of-the-art methods by 6.1% and 4.1%, respectively. On SEED-V, the model demonstrated robust multi-class performance, with an average accuracy of 89.9%, including 96.1% for disgust and 93.6% for happy emotions, outperforming baseline methods. Furthermore, the model’s inference latency of 67.10 ms per 1-second epoch shows its capability to support real-time emotion classification, making it suitable for brain-computer interaction (BCI) and HCI applications.

Source: Cognitive Computation

DOI: 10.1007/s12559-026-10585-8

Authors: M W Geda, Yuk Ming Tang, Carman K. M. Lee, Chen Lerui

Institutions: Hong Kong Polytechnic University, Zhongyuan University of Technology

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,090

Funder: Hong Kong Polytechnic University