Electroencephalography (EEG) has emerged as a valuable non‐invasive tool for examining neural dynamics and recognising patterns associated with depressive disorders. Recent advances in deep learning ...

A deep-learning model achieved significantly higher accuracy and F1-scores for both Cognitive Abilities Screening Instrument and Digit Symbol Coding Test. A deep-learning model vs a comparison model ...

Researchers develop a novel topology-aware multiscale feature fusion network to enhance the accuracy and robustness of EEG-based motor imagery decoding Electroencephalography (EEG) is a fascinating ...

Objective To develop a clinically feasible, brain-based tool for sideline concussion classification. Design Prospective cohort study. Setting High school athletes. Participants 35 healthy control male ...

An overview of attention detection using EEG signals, which includes six steps: an experimental paradigm design, in which the task and the stimuli are defined and presented to the subjects; EEG data ...

Please provide your email address to receive an email when new articles are posted on . INDIANAPOLIS — Machine learning models predict the likelihood of type 1 narcolepsy with a high degree of ...

Treatment-resistant depression is an immense source of disability and suffering, and represents a major unmet clinical need for innovative and effective therapies. Deep brain stimulation trials for ...

Giridhar Kalamangalam does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations ...