A novel and accurate EEG emotion classification model based on multiple attention local binary patterns

Electroencephalography (EEG) signals play a crucial role in understanding brain activity because they provide useful information about real emotions and intentions. Many machine learning models have been used for automatic EEG-based emotion classification. However, previous studies remain limited by restricted feature representations and insufficient subject-independent validation.

In this work, we developed a novel EEG emotion dataset from 22 healthy participants. The dataset includes 14-channel EEG recordings with arousal and valence labels. We also proposed a new feature-extraction function named multiple attention local binary pattern (MATLBP), which generates five feature vectors from EEG signals. To improve the feature-engineering process, we designed an architecture with multi-level MATLBP-based feature extraction, multiple feature-selection methods, and a multi-classifier classification phase. We also used a channel-wise evaluation approach for all EEG channels. Then, iterative majority voting (IMV) was applied during classification to generate predicted vectors. Finally, the MATLBP-based feature-engineering architecture selected the best prediction vector as the final outcome. Thus, the proposed model can automatically select the most accurate result.

The proposed framework was assessed on both the self-collected EEG dataset and the publicly available DREAMER dataset using leave-one-subject-out cross-validation. The model achieved accuracies of 93.38% for arousal and 88.64% for valence on the self-collected dataset. On the DREAMER dataset, it achieved accuracies of 93.56% for arousal, 97.22% for valence, and 86.73% for dominance. Relative to previously reported methods, the proposed approach demonstrated competitive performance under subject-independent validation conditions, with the additional advantage of reduced computational complexity.

The proposed MATLBP-based framework provides an accurate and computationally efficient approach for subject-independent EEG emotion classification across the self-collected and DREAMER datasets. Its strong cross-subject performance and lightweight architecture indicate its potential use in affect-aware clinical decision-support systems, neurofeedback applications, and assistive technologies for individuals with impaired emotional expression. The primary limitation is potential optimistic bias due to model selection on the same dataset. Independent validation in larger, multicenter cohorts is needed to confirm generalizability.