Keywords: classification, finger movements, EEG, feature selection, applied physicsPublished in DKUM: 22.07.2025; Views: 0; Downloads: 6 Full text (1,08 MB)This document has many files! More...
Keywords: brain-computer interfaces, electroencephalogram, feature reduction, machine learning, finger movements classification, time series analysisPublished in DKUM: 16.04.2025; Views: 0; Downloads: 3 Full text (1,44 MB)This document has many files! More...
Keywords: brain-computer interfaces, electroencephalogram, feature and channel investigation, feature selection, machine learning, motor imagery task classificationPublished in DKUM: 19.12.2024; Views: 0; Downloads: 5 Full text (859,70 KB)This document has many files! More...
Abstract: 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. Keywords: brain-computer interfaces, electroencephalogram, feature selection, machine learning, task classificationPublished in DKUM: 10.09.2024; Views: 31; Downloads: 8 Full text (1,15 MB)This document has many files! More...
