Evaluation of classification techniques for identifying cognitive load levels using EEG signals
Version 2 2024-06-05, 11:51Version 2 2024-06-05, 11:51
Version 1 2020-08-21, 12:38Version 1 2020-08-21, 12:38
conference contribution
posted on 2024-06-05, 11:51authored bySyed Moshfeq Salaken, Imali Hettiarachchi, Luke Crameri, Samer HanounSamer Hanoun, Saeid Nahavandi, Thanh Nguyen
Wearable technology is gaining enormous attention among researchers due to their low cost and ease to transfer from laboratory environment to real world applications. In this paper we evaluate the detection of cognitive load using an off the shelf low cost electroencephalography (EEG) device, namely the EMOTIV EPOC+, by utilising four classifiers including random forest, neural network, linear discriminant analysis (LDA) and logistic regression. We relied on automatic power spectral features calculated from the EmotivPro software for evaluation of classifiers. Using power spectral features automatically calculated from the EMOTIVE headset, we show that the cognitive load levels can be efficiently distinguished (reaching upto 95% accuracy) using the Random forest classification method in near real-time at 8 Hz frequency.
History
Location
Virtual Conference
Start date
2020-08-24
End date
2020-08-27
Language
eng
Publication classification
E1 Full written paper - refereed
Title of proceedings
SYSCON2020 : Proceedings of the 14th Annual IEEE International Systems Conference