miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models

TL;DR

This paper introduces MS-iMamba, a novel neural network architecture that effectively captures multi-scale spatiotemporal features from EEG signals for emotion recognition, outperforming existing methods on multiple datasets.

Contribution

The paper proposes a new multi-scale inverted Mamba network that models local-global temporal and spatial interactions without relying on domain-specific features.

Findings

Achieves high classification accuracy on DEAP, DREAMER, and SEED datasets.

Outperforms state-of-the-art methods with only four-channel EEG signals.

Effectively captures multi-scale spatiotemporal features for emotion recognition.

Abstract

EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces. A key challenge lies in extracting discriminative spatiotemporal features from electroencephalogram (EEG) signals. Existing studies often rely on domain-specific time-frequency features and analyze temporal dependencies and spatial characteristics separately, neglecting the interaction between local-global relationships and spatiotemporal dynamics. To address this, we propose a novel network called Multi-Scale Inverted Mamba (MS-iMamba), which consists of Multi-Scale Temporal Blocks (MSTB) and Temporal-Spatial Fusion Blocks (TSFB). Specifically, MSTBs are designed to capture both local details and global temporal dependencies across different scale subsequences. The TSFBs, implemented with an inverted Mamba structure, focus on the interaction between dynamic temporal dependencies and spatial characteristics. The primary advantage of MS-iMamba lies in its ability to leverage reconstructed multi-scale EEG sequences, exploiting the interaction between temporal and spatial features without the need for domain-specific time-frequency feature extraction. Experimental results on the DEAP, DREAMER, and SEED datasets demonstrate that MS-iMamba achieves classification accuracies of 94.86%, 94.94%, and 91.36%, respectively, using only four-channel EEG signals, outperforming state-of-the-art methods.