# TSCL-LwF: A Cross-Subject Emotion Recognition Model via Multi-Scale CNN and Incremental Learning Strategy

## TL;DR

This paper introduces a new model for recognizing emotions from sparse-channel EEG data using a multi-scale CNN and incremental learning, improving accuracy across subjects.

## Contribution

The novel TSCL-LwF model combines multi-scale CNN and Learning without Forgetting for effective cross-subject emotion recognition from sparse-channel EEG.

## Key findings

- TSCL-LwF achieved 77.26% accuracy for valence classification on the DEAP dataset.
- The model showed 80.12% accuracy for arousal classification on the same dataset.
- It also demonstrated superior performance on the self-collected EPPVR dataset.

## Abstract

Background/Objectives: Wearable affective human–computer interaction increasingly relies on sparse-channel EEG signals to ensure comfort and practicality in real-life scenarios. However, the limited information provided by sparse-channel EEG, together with pronounced inter-subject variability, makes reliable cross-subject emotion recognition particularly challenging. Methods: To address these challenges, we propose a cross-subject emotion recognition model, termed TSCL-LwF, based on sparse-channel EEG. It combines a multi-scale convolutional network (TSCL) and an incremental learning strategy with Learning without Forgetting (LwF). Specifically, the TSCL is utilized to capture the spatio-temporal characteristics of sparse-channel EEG, which employs diverse receptive fields of convolutional networks to extract and fuse the interaction information within the local prefrontal area. The incremental learning strategy with LwF introduces a limited set of labeled target domain data and incorporates the knowledge distillation loss to retain the source domain knowledge while enabling rapid target domain adaptation. Results: Experiments on the DEAP dataset show that the proposed TSCL-LwF achieves accuracy of 77.26% for valence classification and 80.12% for arousal classification. Moreover, it also exhibits superior accuracy when evaluated on the self-collected dataset EPPVR. Conclusions: The successful implementation of cross-subject emotion recognition based on a sparse-channel EEG will facilitate the development of wearable EEG technologies with practical applications.