DEPRESSION treatment response was predicted with high accuracy using short segments of resting-state electroencephalography (EEG), according to new research that modelled selective serotonin reuptake ...

These connectivity features were identified through a data-driven method, employing machine learning. We carried out some automatic, moderate pre-processing and extracted spectral connectivity ...

Abstract: PD is progressive neurodegenerative ailment that significantly affects life quality owing to death of dopamine-generating neurons in brain's area of substantia nigra. Symptoms include ...

Abstract: Electroencephalogram (EEG) analysis is a critical tool for diagnosing various neurological disorders. Intelligent EEG models facilitate the analysis and diagnosis of these conditions.

Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies ...

Machine learning (ML) is the branch of artificial intelligence (AI) that develops computational systems that learn from experience. In supervised ML, the ML system generalizes from labelled examples ...

Brain activation in response to spoken motor commands can be detected by electroencephalography (EEG) in clinically unresponsive patients. The prevalence and prognostic importance of a dissociation ...