Statistically significant features improve binary and multiple motor imagery task predictions from EEGsDegirmenci, Murside (Avtor) Yuce, Yilmaz Kemal (Avtor) Perc, Matjaž (Avtor) Isler, Yalcin (Avtor) brain-computer interfaceselectroencephalogramfeature selectionmachine learningtask classificationIn recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require. Frontiers Media20232024-04-11 03:55:29Članek v reviji88197UDK: 53:004.85COBISS_ID: 158876931DOI: 10.3389/fnhum.2023.1223307ISSN pri članku: 1662-5161sl