Multiclass Classification of Spatially Filtered Motor Imagery EEG Signals Using Convolutional Neural Network for BCI Based Applications
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Nijisha Shajil | Sasikala Mohan | Poonguzhali Srinivasan | Janani Arivudaiyanambi | Arunnagiri Arasappan Murrugesan | Sasikala Mohan | Poonguzhali Srinivasan | Nijisha Shajil | Janani Arivudaiyanambi | Arunnagiri Arasappan Murrugesan
[1] Yue Liu,et al. Study on Classification of Left-Right Hands Motor Imagery EEG Signals Based on CNN , 2018, 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC).
[2] Richard K. G. Do,et al. Convolutional neural networks: an overview and application in radiology , 2018, Insights into Imaging.
[3] Ji-Woong Choi,et al. Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain–computer interface: three-class classification of rest, right-, and left-hand motor execution , 2017, Neurophotonics.
[4] John Thomas,et al. Deep learning-based classification for brain-computer interfaces , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[5] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[6] Chungang Yan,et al. Deep convolutional neural network for decoding motor imagery based brain computer interface , 2017, 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC).
[7] Moritz Grosse-Wentrup,et al. Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction , 2008, IEEE Transactions on Biomedical Engineering.
[8] K.-R. Muller,et al. Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.
[9] Haya Alaskar. Deep Learning of EMG Time–Frequency Representations for Identifying Normal and Aggressive Actions , 2018 .
[10] Nuri Korhan,et al. Motor Imagery Based EEG Classification by Using Common Spatial Patterns and Convolutional Neural Networks , 2019, 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT).
[11] Erol Başar,et al. The CLAIR model: Extension of Brodmann areas based on brain oscillations and connectivity. , 2016, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[12] Vacius Jusas,et al. Application of Convolutional Neural Networks to Four-Class Motor Imagery Classification Problem , 2017, Inf. Technol. Control..
[13] G. Pfurtscheller,et al. EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.
[14] Jonathan R. Wolpaw,et al. Brain–Computer Interfaces: Something New under the Sun , 2012 .
[15] Cuntai Guan,et al. On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[16] Haiping Lu,et al. Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting , 2010, IEEE Transactions on Biomedical Engineering.
[17] Young-Seok Choi,et al. A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image , 2018, 2018 International Conference on Information Networking (ICOIN).
[18] Shouqian Sun,et al. Single-trial EEG classification of motor imagery using deep convolutional neural networks , 2017 .
[19] Fuchun Sun,et al. Asynchronous brain-computer interface shared control of robotic grasping , 2019, Tsinghua Science and Technology.
[20] Dawei Hong,et al. Dynamics of high frequency brain activity , 2017, Scientific Reports.
[21] Abdulkadir Sengur,et al. Convolutional Neural Network Based Approach Towards Motor Imagery Tasks EEG Signals Classification , 2019, IEEE Sensors Journal.
[22] Sergio Cruces,et al. Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison , 2018, Entropy.
[23] Yurong Liu,et al. A survey of deep neural network architectures and their applications , 2017, Neurocomputing.
[24] A Subiel,et al. An ultra-high gain and efficient amplifier based on Raman amplification in plasma , 2017, Scientific Reports.
[25] Pascal Frossard,et al. Adaptive data augmentation for image classification , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[26] Tonio Ball,et al. Deep learning with convolutional neural networks for decoding and visualization of EEG pathology , 2017, 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).
[27] Shuicheng Yan,et al. Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[28] Daniel L. Rubin,et al. Differential Data Augmentation Techniques for Medical Imaging Classification Tasks , 2017, AMIA.
[29] Jonathan R. Wolpaw,et al. Brain–Computer InterfacesPrinciples and Practice , 2012 .
[30] Jordi Grau-Moya,et al. Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments , 2017, Entropy.
[31] M. K. M. Rahman,et al. A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison , 2017 .
[32] Ugur Halici,et al. A novel deep learning approach for classification of EEG motor imagery signals , 2017, Journal of neural engineering.