EEG-based depression detection using a local–global feature fusion deep learning network

Key Points

• Depression detection accuracy reached 92% with this method, proving its potential utility.
• Key evidence shows local-global feature fusion enhances EEG data interpretation significantly.
• Analysis utilized a feature fusion deep learning network to improve classification of EEG signals.
• This approach may enable better mental health assessments, indicating a need for broader testing in diverse settings.