@inproceedings{scholars11824,
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings},
           title = {EEG visual and non- Visual learner classification using LSTM recurrent neural networks},
           pages = {467--471},
            note = {cited By 3; Conference of 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 ; Conference Date: 3 December 2018 Through 6 December 2018; Conference Code:144644},
            year = {2019},
             doi = {10.1109/IECBES.2018.08626711},
          author = {Jawed, S. and Amin, H. U. and Malik, A. S. and Faye, I.},
            isbn = {9781538624715},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062783413&doi=10.1109\%2fIECBES.2018.08626711&partnerID=40&md5=86f34114a4e941db77089975febaa9d4},
        keywords = {Biomedical engineering; Electroencephalography; Feature extraction; Learning systems, Back-ground knowledge; Feature classification; Learning and memory; Learning Style; LSTM RNN; Recurrent neural network (RNN); University students; Visual learners, Long short-term memory},
        abstract = {The purpose of this study is to distinguish the visual learners from non-visual learners while learning, having no background knowledge of the contents. The learners are distinguished analysing their brain patterns. EEG data were recorded during learning and memory tasks using 128 channels machine from a sample of thirty -four healthy university students. The students were shown the animated learning content in video format for eight minutes. The brain waves were measured during learning task. The study characterizes and distinguishes between the visual learners and non-visual learners considering the extracted brain patterns. The wavelet features are computed for the recorded EEG and are filtered into alpha and beta sub bands. These features are then given as an input to the Long-Short Term Memory (LSTM) Recurrent neural network (RNN). Feature classification using LSTM Recurrent neural network has attained training accuracy of 89 and 85 for beta and alpha bands for Learning session 1(Learning 1), 86 and 87 for Learning session 2(Learning 2). {\^A}{\copyright} 2018 IEEE.}
}