EEG-based prediction of driving performance using spectrograms and vision transformers

Cognitive lapses during driving are a leading cause of road accidents, underscoring the need for systems capable of online monitoring of attention and performance. While previous studies have used EEG to assess driver fatigue or focus, most approaches rely on raw signals or handcrafted features, limiting their ability to capture two-dimensional patterns underlying driving performance states. To address this limitation, we propose a deep learning framework that integrates advanced EEG preprocessing with a Vision Transformer (ViT) for predicting driving performance. EEG data from 21 participants performing a simulated driving task were cleaned using ICA to remove artifacts, followed by the extraction of event-related desynchronization/synchronization (ERDS) spectrograms to capture spectral dynamics around driving perturbations. The spectrograms were further enhanced using edge-focused image preprocessing techniques and used as input to a ViT classifier. Driving performance was labeled based on reaction times to lateral perturbations and categorized into good or poor performance. The proposed model achieved 81.8% classification accuracy and 89.8% AUC, significantly outperforming models trained on raw EEG signals and handcrafted features. These findings demonstrate the potential of combining EEG-based spectro-temporal representations with transformer architectures for accurate assessment of cognitive states in driving environments, with potential applicability to real-time systems. • ViT-based deep model for classifying driving performance from EEG signals. • Event-related EEG spectrograms improve cognitive state decoding accuracy. • Edge-focused preprocessing enhances spectrogram image discriminability. • Combining ERDS spectrograms with ViTs boosts driving performance prediction.