EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm

Biomedical Signal Processing and Control - Tập 60 - Trang 101989 - 2020
Debashis Das Chakladar1, Shubhashis Dey2, Partha Pratim Roy1, Debi Prosad Dogra3
1Dept. of Computer Science and Engineering, Indian Institute of Technology Roorkee, India
2Dept. of Computer Science and Engineering, Institute of Engineering & Managament, Kolkata, India
3School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, India

Tài liệu tham khảo

Al-Shargie, 2018, Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach, Med. Biol. Eng. Comput., 56, 125, 10.1007/s11517-017-1733-8 Blanco, 2016, Quantifying cognitive workload in simulated flight using passive, dry EEG measurements, IEEE Trans. Cognit. Dev. Syst., 10, 373, 10.1109/TCDS.2016.2628702 Borghini, 2014, Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness, Neurosci. Biobehav. Rev., 44, 58, 10.1016/j.neubiorev.2012.10.003 Bratfisch, 2008 Budak, 2019, An effective hybrid model for EEG-based drowsiness detection, IEEE Sens. J., 10.1109/JSEN.2019.2917850 Charbonnier, 2016, EEG index for control operators’ mental fatigue monitoring using interactions between brain regions, Expert Syst. Appl., 52, 91, 10.1016/j.eswa.2016.01.013 Deb, 2002, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182, 10.1109/4235.996017 Dyer, 2015 Fallahi, 2016, Psycho physiological and subjective responses to mental workload levels during n-back task, J. Ergon., 6, 2 Graves, 2005, Framewise phoneme classification with bidirectional lstm and other neural network architectures, Neural Netw., 18, 602, 10.1016/j.neunet.2005.06.042 Hart, 1988, Development of NASA-TLX (task load index): results of empirical and theoretical research, vol. 52, 139 Hefron, 2017, Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation, Pattern Recognit. Lett., 94, 96, 10.1016/j.patrec.2017.05.020 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Kennedy, 2010, Particle swarm optimization, Encycl. Mach. Learn., 760 Kuanar, 2018, Cognitive analysis of working memory load from EEG, by a deep recurrent neural network Lim, 2018, Simultaneous task EEG workload data set, IEEE Trans. Neural Syst. Rehabil. Eng., 26, 2106, 10.1109/TNSRE.2018.2872924 Liu, 2018, EEG-based evaluation of mental fatigue using machine learning algorithms, 276 Marinescu, 2018, Physiological parameter response to variation of mental workload, Hum. Factors, 60, 31, 10.1177/0018720817733101 Mirjalili, 2014, Grey wolf optimizer, Adv. Eng. Softw., 69, 46, 10.1016/j.advengsoft.2013.12.007 Nguyen, 2015, Deep neural networks are easily fooled: high confidence predictions for unrecognizable images, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 427 Reid, 1988, The subjective workload assessment technique: a scaling procedure for measuring mental workload, vol. 52, 185 Roy, 2016, Efficient mental workload estimation using task-independent EEG features, J. Neural Eng., 13, 026019, 10.1088/1741-2560/13/2/026019 Saadati, 2019, Convolutional neural network for hybrid fNIRS-EEG mental workload classification, International Conference on Applied Human Factors and Ergonomics, 221 Schirrmeister, 2017, Deep learning with convolutional neural networks for eeg decoding and visualization, Hum. Brain Mapp., 38, 5391, 10.1002/hbm.23730 Wang, 2013, Real-time mental arithmetic task recognition from EEG signals, IEEE Trans. Neural Syst. Rehabil. Eng., 21, 225, 10.1109/TNSRE.2012.2236576 Wang, 2010, Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain, Nonlinear Biomed. Phys., 4, 2, 10.1186/1753-4631-4-2 Wilson, 2002, An analysis of mental workload in pilots during flight using multiple psychophysiological measures, Int. J. Aviat. Psychol., 12, 3, 10.1207/S15327108IJAP1201_2 Xu, 2015, Learning temporal features using LSTM-CNN architecture for face anti-spoofing, 141 Yan, 2017, Effect of user interface layout on the operators’ mental workload in emergency operating procedures in nuclear power plants, Nucl. Eng. Des., 322, 266, 10.1016/j.nucengdes.2017.07.012 Zabalza, 2016, Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging, Neurocomputing, 185, 1, 10.1016/j.neucom.2015.11.044 Zhang, 2018, Learning spatial-spectral-temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment, IEEE Trans. Neural Syst. Rehabil. Eng., 27, 31, 10.1109/TNSRE.2018.2884641 Zhao, 2019, Speech emotion recognition using deep 1D & 2D CNN LSTM networks, Biomed. Signal Process. Control, 47, 312, 10.1016/j.bspc.2018.08.035 Zhong, 2017, Cross-subject classification of mental fatigue by neurophysiological signals and ensemble deep belief networks