EEG-based intention recognition with deep recurrent-convolution neural network: Performance and channel selection by Grad-CAM
Tài liệu tham khảo
Vidal, 1977, Real-time detection of brain events in eeg, Proc. IEEE, 65, 633, 10.1109/PROC.1977.10542
Birbaumer, 2003, The thought-translation device (ttd): neurobehavioral mechanisms and clinical outcome, IEEE Trans. Neural Syst. Rehabilit. Eng., 11, 120, 10.1109/TNSRE.2003.814439
Scherer, 2008, Toward self-paced brain–computer communication: navigation through virtual worlds, IEEE Trans. Biomed. Eng., 55, 675, 10.1109/TBME.2007.903709
Wolpaw, 2002, Brain–computer interfaces for communication and control, Clin. Neurophysiol., 113, 767, 10.1016/S1388-2457(02)00057-3
Vaughan, 2006, The wadsworth bci research and development program: at home with bci, IEEE Trans. Neural Syst. Rehabilit. Eng., 14, 229, 10.1109/TNSRE.2006.875577
Hsu, 2011, Continuous eeg signal analysis for asynchronous bci application, Int. J. Neural Syst., 21, 335, 10.1142/S0129065711002870
Hill, 2006, Classifying eeg and ecog signals without subject training for fast bci implementation: comparison of nonparalyzed and completely paralyzed subjects, IEEE Trans. Neural Syst. Rehabilit. Eng., 14, 183, 10.1109/TNSRE.2006.875548
Lemm, 2005, Spatio-spectral filters for improving the classification of single trial eeg, IEEE Trans. Biomed. Eng., 52, 1541, 10.1109/TBME.2005.851521
Hsu, 2007, Wavelet-based fractal features with active segment selection: application to single-trial eeg data, J. Neurosci. Methods, 163, 145, 10.1016/j.jneumeth.2007.02.004
Lemm, 2006, Enhancing the signal-to-noise ratio of ica-based extracted erps, IEEE Trans. Biomed. Eng., 53, 601, 10.1109/TBME.2006.870258
A. Krizhevsky, I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105.
Karpathy, 2014, Large-scale video classification with convolutional neural networks, 1725
Graves, 2013, Speech recognition with deep recurrent neural networks, 6645
LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791
Chen, 2018, Eeg-based motion intention recognition via multi-task rnns, 279
Lawhern, 2018, Eegnet: a compact convolutional neural network for eeg-based brain–computer interfaces, J. Neural Eng., 15, 056013, 10.1088/1741-2552/aace8c
Zhang, 2017, Intent recognition in smart living through deep recurrent neural networks, 748
D. Zhang, L. Yao, X. Zhang, S. Wang, W. Chen, R. Boots, and B. Benatallah, Cascade and parallel convolutional recurrent neural networks on eeg-based intention recognition for brain computer interface, in: Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
Tang, 2017, Memory visualization for gated recurrent neural networks in speech recognition, 2736
Kim, 2017, Acoustic event detection in multichannel audio using gated recurrent neural networks with high-resolution spectral features, ETRI J., 39, 832, 10.4218/etrij.17.0117.0157
Arvaneh, 2011, Optimizing the channel selection and classification accuracy in eeg-based bci, IEEE Trans. Biomed. Eng., 58, 1865, 10.1109/TBME.2011.2131142
A. Chatchinarat, K.W. Wong, C.C. Fung, A comparison study on the relationship between the selection of eeg electrode channels and frequency bands used in classification for emotion recognition, in: 2016 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, IEEE, 2016, pp. 251–256.
Thomas, 2018, Eeg-based biometric authentication using gamma band power during rest state, Circ., Syst., Signal Process., 37, 277, 10.1007/s00034-017-0551-4
Selvaraju, 2017, Grad-cam: visual explanations from deep networks via gradient-based localization, 618
Goldberger, 2000, Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals, Circulation, 101, e215, 10.1161/01.CIR.101.23.e215
Schalk, 2004, Bci2000: a general-purpose brain-computer interface (bci) system, IEEE Trans. Biomed. Eng., 51, 1034, 10.1109/TBME.2004.827072
Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, ”Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.
Zhou, 2016, Learning deep features for discriminative localization, 2921
Chen, 2019, Use of deep learning to detect personalized spatial-frequency abnormalities in eegs of children with adhd, J. Neural Eng., 16, 066046, 10.1088/1741-2552/ab3a0a
Jonas, 2019, Eeg-based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features, Human Brain Mapp., 40, 4606, 10.1002/hbm.24724
Ye, 2019, Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network, Eur. Radiol., 29, 6191, 10.1007/s00330-019-06163-2
Zeng, 2019, An improved particle filter with a novel hybrid proposal distribution for quantitative analysis of gold immunochromatographic strips, IEEE Trans. Nanotechnol., 18, 819, 10.1109/TNANO.2019.2932271
Zeng, 2020, Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip, Neurocomputing
Zeng, 2014, Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach, IEEE Trans. Med. Imag., 33, 1129, 10.1109/TMI.2014.2305394
D.P. Kingma, J. Ba, Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980, 2014.
Alomari, 2014, Eeg mouse: a machine learning-based brain computer interface, Int. J. Adv. Comput. Sci. Appl., 5, 193
Sita, 2013, Feature extraction and classification of eeg signals for mapping motor area of the brain, 463
Zhang, 2019, Know your mind: adaptive cognitive activity recognition with reinforced cnn, 896