| Abstrakt: |
The cognitive workload is a key to developing a logical and conscious thinking system. Maintaining an optimum workload improves the performance of an individual. The individuals' psycho-social factors are responsible for creating significant variability in the performance of a task, which poses a significant challenge in developing a consistent model for the classification of cross-task cognitive workload using physiological signal, Electroencephalogram (EEG). The primary focus of the proposed work is to develop a robust classification model CARNN, by employing the concatenated deep structure of distributed branches of convolutional neural networks with residual blocks through identity mappings, and recurrent neural network with an attention mechanism. EEG data is divided into milliseconds duration overlap segments. The segmented EEG data is converted into images using Gabor decomposition with two spatial frequency scales and four orientations and supplied as input to CARNN. The images are formed by interlacing the respective left and right electrode data to capture the data variations effectively. Efficient feature aggregation with learning of spatial and temporal domain discriminative features through Gabor decomposed data images improve the training of CARNN. CARNN achieves outstanding performance over traditional classifiers; support vector machine, k-nearest neighbor (KNN), ensemble subspace KNN and the pre-trained networks; AlexNet, ResNet18/50, VGG16/19, and Inception-v3. The proposed method results in 94.2%, 92.5%, 95.9%, 92.8%, 94.3% classification accuracy, specificity, sensitivity, precision, and F1-score, respectively. Two visual task levels apart in their complexity are used for cross-task classification of cognitive workload. The proposed method is validated on raw EEG data of 44 participants. [ABSTRACT FROM AUTHOR] |
