EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients’ Rehabilitation

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 8; p. 3889
• Main Authors: Al-Qazzaz, Noor Kamal, Aldoori, Alaa A., Ali, Sawal Hamid Bin Mohd, Ahmad, Siti Anom, Mohammed, Ahmed Kazem, Mohyee, Mustafa Ibrahim
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.04.2023; MDPI
• Subjects: Algorithms; brain computer interface; Brain research; Brain-Computer Interfaces; Classification; Data processing; Dynamical systems; electroencephalogram; Electroencephalography; Electroencephalography - methods; entropy; features; Forecasts and trends; Humans; Imagery, Psychotherapy; independent component analysis; Medical research; Medicine, Experimental; motor imagery; Neurophysiology; Patients; Quality of life; Rehabilitation; Stroke; Stroke patients; Stroke Rehabilitation; Variance analysis; Malaysia; features; independent component analysis; entropy; brain computer interface; motor imagery; classification; Laplacian; electroencephalogram

The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the...