TY - GEN
T1 - EEG-based Estimation of Cognitive Workload Across Multiple Tasks
AU - Mathew, Anita Susan
AU - Hirachan, Niraj
AU - Joseph, Calvin
AU - Ghahramani, Maryam
AU - Lopez-Aparicio, Jehu
AU - Rojas, Raul Fernandez
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Key to the efficacy of working in high-risk environments is the reliable estimation of the human's cognitive state for improving safety and to maintain high performance longer. In this study, we developed an experimental protocol in which participants completed three cognitive tasks under two different levels (High, Low) of workload. We then evaluated the effect of the different cognitive activities on EEG signals and its accuracy in predicting respective cognitive load. The analysis was conducted using well-known machine learning algorithms such as SVM, RF, and KNN. An average accuracy of 82.75% was obtained through the proposed SVM model to identify the participant's cognitive workload level. The results obtained through this study indicated the efficacy of the EEG features in predicting the level of cognitive load irrespective of the activity. The proposed set of EEG features represents the cognitive indicators that form the basis for developments of augmented cognition systems in our future works.
AB - Key to the efficacy of working in high-risk environments is the reliable estimation of the human's cognitive state for improving safety and to maintain high performance longer. In this study, we developed an experimental protocol in which participants completed three cognitive tasks under two different levels (High, Low) of workload. We then evaluated the effect of the different cognitive activities on EEG signals and its accuracy in predicting respective cognitive load. The analysis was conducted using well-known machine learning algorithms such as SVM, RF, and KNN. An average accuracy of 82.75% was obtained through the proposed SVM model to identify the participant's cognitive workload level. The results obtained through this study indicated the efficacy of the EEG features in predicting the level of cognitive load irrespective of the activity. The proposed set of EEG features represents the cognitive indicators that form the basis for developments of augmented cognition systems in our future works.
UR - http://www.scopus.com/inward/record.url?scp=85214987172&partnerID=8YFLogxK
UR - https://embc.embs.org/2024/
UR - https://ieeexplore.ieee.org/xpl/conhome/10781475/proceeding
U2 - 10.1109/EMBC53108.2024.10782830
DO - 10.1109/EMBC53108.2024.10782830
M3 - Conference contribution
AN - SCOPUS:85214987172
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1
EP - 4
BT - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
A2 - Jung, Renu
A2 - Wheeler, Bruce
A2 - Otto, Kevin
A2 - Fernanda Cabrera-Umpiérrez, María
A2 - Mitsis, Georgios
A2 - Wang, May
A2 - Chan, Rose
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Y2 - 15 July 2024 through 19 July 2024
ER -