Arslan, Mustafa TuranYildirim, Esen2025-01-062025-01-0620242149-914410.22399/ijcesen.3282-s2.0-85201595185https://doi.org/10.22399/ijcesen.328https://hdl.handle.net/20.500.14669/1557This study investigates the classification of electrical brain activity during intensive-less intensive and related-unrelated tasks. EEG signals were collected from 20 physically and mentally healthy university students (15 males, 5 females) residing in Adana and Hatay, Turkey, through 14 channels. Continuous Wavelet Transform analysis was applied for feature extraction. Subsequently, subject-dependent and subject-independent classifications were performed using the k-nearest Neighbour algorithm. In subjectdependent classification, the accuracy range for intensive-less intensive tasks varied between 77.6% and 89.8%, while the range for related-unrelated tasks was between 73.2% and 88%. Subject-independent classification yielded an accuracy of 79.2% for intensive-less intensive tasks and 77.5% for related unrelated tasks. © IJCESENeninfo:eu-repo/semantics/openAccessClassificationContinuousWavelet TransformEEGk-nearest NeighborArticle2272Q422110