Cross-task, explainable and real-time decoding of human emotion states by integrating gray and white matter intracranial neural activity.

Decoding human emotion states from intracranial neural activity is key in developing affective brain-computer interfaces and new therapies for affective disorders. However, real-world application of decoding requires high performance that integrates neural activity from both gray and white matter, stable generalization across different contexts, sufficient neural encoding explainability and robust real-time implementation, all of which remain elusive. Here we simultaneously recorded intracranial electroencephalogram (iEEG) and abundant self-rated valence and arousal scores across two emotion-eliciting tasks in 18 individuals. We then developed personalized decoding models within a deep learning framework, achieving high-performance decoding of continuous valence and arousal states and improving on the performance of prior EEG and iEEG decoding. Critically, the models substantially improved performance by integrating gray and white matter signals and demonstrated cross-task generalization. The models further revealed shared and preferred mesolimbic-thalamo-cortical subnetworks encoding valence and arousal, showing neurophysiological explainability. Finally, the models realized robust real-time decoding in four new individuals. Our results have implications for advancing emotion decoding neurotechnology toward deployable affective brain-computer interfaces and closed-loop therapeutic systems for affective disorders.