Key result
The K-Nearest Neighbor (KNN) algorithm achieved a classification accuracy of 0.94 for determining increased mental workload from EEG data, outperforming Decision Tree and SVM techniques (0.88).
Why the study?
Extracting information hidden in brain electrical signals can enhance the classification of current mental status.
Absolute Event Rate: 94% vs 88%
High classification accuracies can be obtained in determining increased mental workload states from EEG using K-Nearest Neighbors even when concatenating all subjects' data.
KNN-based EEG classification may support cognitive monitoring in cardiac care; leaves open prospective validation before clinical use.
Extraction of the information hidden in the brain electrical signal enhance the classification of the current mental status. In this study, 16 channel EEG data were collected from 15 volunteers under three conditions. Participants were asked to rest with eyes open and eyes closed states each with a duration of three minutes. Finally, a task has been imposed to increase mental workload. EEG data were epoched with a duration of one second and power spectrum was computed for each time window. The power spectral features of all channels in traditional bands were calculated for all subjects and the results were concatanated to form the input data to be used in classification. Decision tree, K-nearest neighbor and Support Vector Machine techniques were implemented in order to classify the one second epochs. The accuracy value obtained from KNN was found to be 0.94 while it was 0.88 for decision tree and SVM. KNN was found to outperform the two methods when all channel and power spectral features were used. In can be concluded that, even with the use of input features formed by concatanating all subject’s data, high classification accuracies can be obtained in the determination of the increased mental workload state.
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Adil Deniz Duru (2019) studied Mental Workload (n=15). K-Nearest Neighbor (KNN) algorithm vs. Support Vector Machine (SVM) and Decision Tree algorithms was evaluated on Classification accuracy. The K-Nearest Neighbor (KNN) algorithm achieved a classification accuracy of 0.94 for determining increased mental workload from EEG data, outperforming Decision Tree and SVM techniques (0.88).
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