DualAttentionNet: A Lightweight Dual-Attention Framework for Robust Multi-Class EEG Digit Decoding
Abstract
Abstract The visual classification of a stimulus by electroencephalography (EEG) has been a significant line of investigation in Brain Computer Interface (BCI) systems. The current issue of converting EEG signals into useful cognitive representations, including digit recognition, is a difficult issue because of low signal to noise ratio, high inter trial variability and overlapping neural tracks. This paper presents a DualAttentionNet with a multi-class EEG digit classification system based on Visual MNIST. The offered model improves the traditional EEGNet model by incorporating two channel-attention blocks. The architectural design attains 92.36\% classification accuracy, being superior to all the architectures compared and at the same time with 92.36 computational efficiency. Under the analysis of signal variability and explainability, statistical analysis indicates the strength of the model. It is a strong, interpretable and reproducible multi-class EEG-based classification framework. The outcomes emphasise the significance of organised attention systems and streamlined training interventions in enhancing the performance of EEG decoding. The code is available at: https://github.com/mrirashid/EEG-Based-MNIST-Digit-Classification.
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