Akademik Veri Yönetim Sistemi
NEURAL COMPUTING AND APPLICATIONS, cilt.36, sa.18, ss.10915-10926, 2024 (SCI-Expanded)
Cognitive fatigue occurs in various situations and is an essential condition to detect. In this study, how single and multi-tasking tests affect cognitive workload was examined, and multi-tasking was detected using electroencephalography (EEG) signals. In the cognitive workload paradigm, single-task tests with blocks 1 and 2 and multi-tasking tests with block 3 were created. EEG signals obtained from these blocks were treated as different frequency bands and lengths, and binary classification was performed. Two binary classifications were made: block 1–block 3 and block 2–block 3. According to the results, the highest classification accuracy for block 1–block 3 was obtained as 97.11% using the gamma frequency band and 5-s EEG length. For block 2–block 3, the highest classification accuracy was obtained as 90.88% using the gamma frequency band and 5-s EEG length. As a result, this study distinguished multi-tasking and single task with high classification accuracy. The developed model can be used to detect attention deficit and inability to focus. In addressing the prevalent challenges of distinguishing cognitive fatigue in single—task versus multitasking scenarios, our study offers a new method, which achieve a remarkable accuracy rate, thereby illuminating a new path in the research of cognitive fatigue.