Systematic Comparison of Electroencephalography Feature Domains for Visual Stimuli Decoding with EEGNet and EEG Conformer

Electroencephalography-based visual decoding has important applications in brain–computer interfaces and cognitive neuroscience, yet the relative effectiveness of different feature extraction methods for sustained visual paradigms remains unclear due to the absence of standardized, multi-dataset comparative evaluations. This study systematically compares eight feature extraction methods across three public EEG datasets: MindBigData MNIST, MindBigData MNIST-8B for digit recognition, and MSS for natural image classification. The methods include coherence, Granger causality, directed transfer function, partially directed coherence, transfer entropy, discrete wavelet transform, empirical wavelet transform (EWT), and wavelet scattering transform. Two deep learning architectures, EEGNet and EEG Conformer, were trained using two pre-processing pipelines, with and without artifact removal. EWT achieved the highest classification accuracy, reaching 97.83% for digit-vs-blank and 77.10% for within-session natural image classification. Connectivity-based methods consistently underperformed, with the best connectivity method (coherence) reaching up to 91.67%, suggesting that spectral power information is more discriminative than inter-channel relationships. Cross-subject generalization remained challenging, with best accuracies near 68%. The findings establish wavelet-based adaptive spectral decomposition as a strong baseline for EEG visual decoding and highlight the need for domain adaptation techniques to address cross-subject variability.