Classification of Intensive-less Intensive and Related-Unrelated TasksTasks
DOI:
https://doi.org/10.22399/ijcesen.328Keywords:
Classification, EEG, Continuous Wavelet Transform, k-nearest NeighborAbstract
This 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 Neighbor algorithm. In subject-dependent 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.
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