Revealing EEG signatures of intervention in disorder of consciousness using artificial intelligence: methodology and feasibility.

BACKGROUND AND OBJECTIVE: Electroencephalography (EEG) is a crucial tool for monitoring recovery in patients with disorders of consciousness (DOC) after therapeutic interventions. It helps in identifying the neural correlates and in guiding the development of personalized treatments. Spectrum power measures are widely employed. However, these measures are manually handcrafted, not patient-specific, and not tailored to the specific intervention. METHODS: To address these limitations, we propose an explainable artificial intelligence (XAI) framework designed to automatically uncover the most salient frequency-domain EEG signatures in an intervention- and patient-specific manner. The framework integrates an interpretable convolutional neural network, which is capable of learning interpretable frequency-domain EEG features, with an explanation technique, which quantifies the relevance of the learned spectral features. This approach enables the automatic tracking of patient-specific spectral EEG changes and refines the analysis toward neural features that are more closely associated with key clinical variables. RESULTS: We showcase the potential of our approach by applying it to EEG signals collected from patients in a minimally conscious state following an intervention based on transcranial direct current stimulation. The XAI results reveal a prominent role of alpha-band EEG oscillations in DOC intervention, supporting evidence that functional improvements due to intervention are associated with an increase in alpha-band spectral content. CONCLUSIONS: Our XAI-driven analysis offers a robust, individualized, and transparent alternative (or complement) to conventional EEG analyses, thereby enhancing the EEG characterization of DOC patients.