20.500.12556/DKUM-88197 Statistically significant features improve binary and multiple motor imagery task predictions from EEGs In 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. brain-computer interfaces electroencephalogram feature selection machine learning task classification vmesnik računalnik - možgani elektroencefalogram izbira lastnosti strojno učenje klasifikacija nalog true true true Frontiers Media Angleški jezik Slovenski jezik Članek v reviji 2024-04-11 03:55:29 2024-09-10 13:20:11 2024-09-11 03:47:00 0000-00-00 00:00:00 2023 0 0 16 str. [article no.] ǂ1223307 Vol. 17 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-05-15 2023-06-23 2023-06-11 53:004.85 158876931 10.3389/fnhum.2023.1223307 1662-5161 RAZ_Degirmenci_Murside_2023.pdf RAZ_Degirmenci_Murside_2023.pdf 1 9E01048282ACF1CE653EB4C30E4D6F22 7c336e8dc48492c8ea15c12062745fdd1132ebde49f92db63054ab79e8ceb67d 7e064bb5-6f5b-11ef-b637-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=184809 //10.3389/fnhum.2023.1223307 0 a81fa517-6f63-11ef-b637-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=184820 Fakulteta za naravoslovje in matematiko 0 0 0