Decoding covert visual attention of electroencephalography signals using continuous wavelet transform and deep learning approach

Covert visual attention decoding from EEG signals is a key challenge in cognitive neuroscience and brain-computer interface applications. Traditional approaches often rely on manual feature extraction and handcrafted pipelines, which limit scalability and generalization. In this study, we propose a deep learning-based framework that leverages time-frequency representations, specifically Continuous Wavelet Transform (CWT), to enable end-to-end classification of covert attention states without manual feature engineering. EEG data were recorded from ten healthy participants performing spatial and feature-based attention tasks. Among the tested models, ShallowConvNet achieved 100% accuracy in binary classification and over 90% in four-class conditions. EEGNet also performed competitively, exceeding 97% and 88% accuracy in two- and four-class scenarios, respectively. These findings demonstrate that integrating CWT with deep neural architectures significantly enhances decoding performance compared to conventional raw-signal approaches, offering a scalable and efficient solution for real-time attention monitoring.