@inproceedings{scholars10138,
         journal = {2018 IEEE 16th Student Conference on Research and Development, SCOReD 2018},
            year = {2018},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
            note = {cited By 16; Conference of 16th IEEE Student Conference on Research and Development, SCOReD 2018 ; Conference Date: 26 November 2018 Through 28 November 2018; Conference Code:148011},
           title = {Classification of EEG Learning and Resting States using 1D-Convolutional Neural Network for Cognitive Load Assesment},
             doi = {10.1109/SCORED.2018.8711150},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066488280&doi=10.1109\%2fSCORED.2018.8711150&partnerID=40&md5=235aeb0fdd354d976dd170ec36c295fe},
            isbn = {9781538691755},
          author = {Qayyum, A. and Khan, M. K. A. A. and Mazher, M. and Suresh, M.},
        keywords = {Brain; Convolution; Discrete wavelet transforms; Feature extraction; Learning algorithms; Neural networks, 1DCNN; Brain wave; Classification technique; Cognitive load measurement; Cognitive state; Convolutional neural network; Resting state; Temporal information, Deep learning},
        abstract = {Classification of resting and cognitive states has its importance in brain neuroscience for understating the underlying behaviors of cognition. The human brain is considered as a complex system having different mental states such as resting, active or cognitive states. It is a well-understood fact that the brain activity increases with the increased demand of cognition. The differences among different mental states can be explored by using classification techniques. The deep learning algorithm based on 1D convolutional neural network (CNN) model has been proposed for the classification of rest and cognitive states and also the cognitive load has been assessed using brain waves particularly alpha wave. EEG data were collected from 34 human participants at resting and during learning state. After preprocessing, EEG data has been segmented into equal number of segments to investigate the deep temporal information for cognitive load assessment. The brain waves were extracted using discrete wavelet transform (DWT) for each segment and fed these segments to proposed model for classification and assessment of cognitive load. The results show that alpha brain wave produced consistent behavior using 1D-CNN technique for cognitive load measurement and provides efficient solution in EEG data for learning and resting state task. {\^A}{\copyright} 2018 IEEE.}
}