Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.

[1]  Bo Hu,et al.  A hierarchical semi-supervised extreme learning machine method for EEG recognition , 2018, Medical & Biological Engineering & Computing.

[2]  Oliver Y. Chén,et al.  Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification , 2018, IEEE Transactions on Biomedical Engineering.

[3]  Benjamin H. Brinkmann,et al.  Large-scale electrophysiology: Acquisition, compression, encryption, and storage of big data , 2009, Journal of Neuroscience Methods.

[4]  Sourav Kundu,et al.  P300 based character recognition using sparse autoencoder with ensemble of SVMs , 2019, Biocybernetics and Biomedical Engineering.

[5]  R. Madhu,et al.  Cat Swarm Fractional Calculus optimization-based deep learning for artifact removal from EEG signal , 2020, J. Exp. Theor. Artif. Intell..

[6]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[7]  M. Shamim Hossain,et al.  Multilevel Weighted Feature Fusion Using Convolutional Neural Networks for EEG Motor Imagery Classification , 2019, IEEE Access.

[8]  Xingyu Wang,et al.  Sparse Bayesian Classification of EEG for Brain–Computer Interface , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[9]  P. Nagabushanam,et al.  EEG signal classification using LSTM and improved neural network algorithms , 2019, Soft Computing.

[10]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[11]  Bao-Liang Lu,et al.  Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks , 2015, IEEE Transactions on Autonomous Mental Development.

[12]  Ikhtiyor Majidov,et al.  Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods , 2019, Sensors.

[13]  Kensuke Kawai,et al.  Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images , 2019, NeuroImage: Clinical.

[14]  Jeonghwan Gwak,et al.  Discrimination of Mental Workload Levels From Multi-Channel fNIRS Using Deep Leaning-Based Approaches , 2019, IEEE Access.

[15]  Stanislas Chambon,et al.  DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal , 2018, Journal of Neuroscience Methods.

[16]  Chao Li,et al.  Canonical Polyadic Decomposition With Auxiliary Information for Brain–Computer Interface , 2014, IEEE Journal of Biomedical and Health Informatics.

[17]  Ugur Halici,et al.  A novel deep learning approach for classification of EEG motor imagery signals , 2017, Journal of neural engineering.

[18]  Fenglong Ma,et al.  A hybrid self-attention deep learning framework for multivariate sleep stage classification , 2019, BMC Bioinformatics.

[19]  Wei Li,et al.  Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition , 2019, Neural Computation.

[20]  Chong Wang,et al.  Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin , 2015, ICML.

[21]  Jianhui Wang,et al.  Densely Feature Fusion Based on Convolutional Neural Networks for Motor Imagery EEG Classification , 2019, IEEE Access.

[22]  Chao Wu,et al.  DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Hyun-Jong Jang,et al.  Dual deep neural network-based classifiers to detect experimental seizures , 2019, The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology.

[24]  Abeer Al-Nafjan,et al.  Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network , 2017 .

[25]  Wei Zhang,et al.  Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders , 2019, Comput. Biol. Medicine.

[26]  Rabab K. Ward,et al.  Semi-Supervised Deep Blind Compressed Sensing for Analysis and Reconstruction of Biomedical Signals From Compressive Measurements , 2018, IEEE Access.

[27]  Seda Arslan Tuncer,et al.  Is it possible to detect cerebral dominance via EEG signals by using deep learning? , 2019, Medical hypotheses.

[28]  Kebin Jia,et al.  A Multi-View Deep Learning Framework for EEG Seizure Detection , 2019, IEEE Journal of Biomedical and Health Informatics.

[29]  Faramarz Safi-Esfahani,et al.  Recognition of words from brain-generated signals of speech-impaired people: Application of autoencoders as a neural Turing machine controller in deep neural networks , 2020, Neural Networks.

[30]  P. Cawthon,et al.  Overview of recruitment for the osteoporotic fractures in men study (MrOS). , 2005, Contemporary clinical trials.

[31]  Zhong Yin,et al.  Cross-session classification of mental workload levels using EEG and an adaptive deep learning model , 2017, Biomed. Signal Process. Control..

[32]  Reda A. El-Khoribi,et al.  Emotion Recognition based on EEG using LSTM Recurrent Neural Network , 2017 .

[33]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[34]  Yoshua Bengio,et al.  Speaker Recognition from Raw Waveform with SincNet , 2018, 2018 IEEE Spoken Language Technology Workshop (SLT).

[35]  Sadasivan Puthusserypady,et al.  An end-to-end deep learning approach to MI-EEG signal classification for BCIs , 2018, Expert Syst. Appl..

[36]  Jianping Zhang,et al.  A deep learning approach for real-time detection of sleep spindles , 2019, Journal of neural engineering.

[37]  Li Wei,et al.  Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition. , 2019 .

[38]  Shaowen Yao,et al.  Deep Fusion Feature Learning Network for MI-EEG Classification , 2018, IEEE Access.

[39]  Miki Haseyama,et al.  Favorite Video Classification Based on Multimodal Bidirectional LSTM , 2018, IEEE Access.

[40]  Kup-Sze Choi,et al.  Walking Imagery Evaluation in Brain Computer Interfaces via a Multi-View Multi-Level Deep Polynomial Network , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[41]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[42]  Kyle I. Ruiter,et al.  Neurophysiological Correlates of Concussion: Deep Learning for Clinical Assessment , 2019, Scientific Reports.

[43]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[44]  Filippo Zappasodi,et al.  Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification , 2018, Journal of neural engineering.

[45]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[46]  Dong Keun Kim,et al.  Arousal and Valence Classification Model Based on Long Short-Term Memory and DEAP Data for Mental Healthcare Management , 2018, Healthcare informatics research.

[47]  Wei Gao,et al.  Multi-ganglion ANN based feature learning with application to P300-BCI signal classification , 2015, Biomed. Signal Process. Control..

[48]  Ihsan Ullah,et al.  An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach , 2018, Expert Syst. Appl..

[49]  Jason Teo,et al.  Classification of Affective States via EEG and Deep Learning , 2018 .

[50]  Laurent Vercueil,et al.  A convolutional neural network for sleep stage scoring from raw single-channel EEG , 2018, Biomed. Signal Process. Control..

[51]  Paolo Favaro,et al.  EEG‐based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features , 2019, Human brain mapping.

[52]  Xinmin Wang,et al.  EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[53]  Raanan Arens,et al.  The Childhood Adenotonsillectomy Trial (CHAT): rationale, design, and challenges of a randomized controlled trial evaluating a standard surgical procedure in a pediatric population. , 2011, Sleep.

[54]  Giulio Ruffini,et al.  Deep Learning With EEG Spectrograms in Rapid Eye Movement Behavior Disorder , 2018, bioRxiv.

[55]  Rabab K. Ward,et al.  Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals , 2019, Clinical Neurophysiology.

[56]  S. Palazzo,et al.  Deep Learning Human Mind for Automated Visual Classification , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[57]  Yang Li,et al.  A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[58]  Yu-Xuan Yang,et al.  A GPSO-optimized convolutional neural networks for EEG-based emotion recognition , 2020, Neurocomputing.

[59]  Partha Pratim Roy,et al.  Age and gender classification using brain–computer interface , 2019, Neural Computing and Applications.

[60]  Yan Song,et al.  A deep learning framework for identifying children with ADHD using an EEG-based brain network , 2019, Neurocomputing.

[61]  Jing Zhu,et al.  Depression recognition using machine learning methods with different feature generation strategies , 2019, Artif. Intell. Medicine.

[62]  Yike Guo,et al.  Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders , 2015, Annals of Biomedical Engineering.

[63]  T. Young,et al.  Nocturnal rapid eye movement sleep latency for identifying patients with narcolepsy/hypocretin deficiency. , 2013, JAMA neurology.

[64]  Fengzhen Tang,et al.  Learning joint space-time-frequency features for EEG decoding on small labeled data , 2019, Neural Networks.

[65]  Haoqi Sun,et al.  Expert-level sleep scoring with deep neural networks , 2018, J. Am. Medical Informatics Assoc..

[66]  Michalis E. Zervakis,et al.  A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals , 2018, Comput. Biol. Medicine.

[67]  Nitish Thakor,et al.  Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[68]  Luca Citi,et al.  Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller , 2010, Journal of neural engineering.

[69]  Martin Spott,et al.  Feature relevance in physiological networks for classification of obstructive sleep apnea , 2018, Physiological measurement.

[70]  Banghua Yang,et al.  Automatic ocular artifacts removal in EEG using deep learning , 2018, Biomed. Signal Process. Control..

[71]  Jianhua Zhang,et al.  Task-generic mental fatigue recognition based on neurophysiological signals and dynamical deep extreme learning machine , 2018, Neurocomputing.

[72]  Translation of EEG-Based Performance Prediction Models to Rapid Serial Visual Presentation Tasks , 2013, HCI.

[73]  Gabriela Moise,et al.  Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques , 2019, Sensors.

[74]  Jianhua Zhang,et al.  Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework , 2019, Neurocomputing.

[75]  Xinbo Gao,et al.  Deep Convolutional Neural Networks for mental load classification based on EEG data , 2018, Pattern Recognit..

[76]  Lei Liu,et al.  Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling , 2019, Symmetry.

[77]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[78]  U. Rajendra Acharya,et al.  Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals , 2019, Journal of Medical Systems.

[79]  Lianqing Zhu,et al.  Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG , 2020, Neurocomputing.

[80]  Carlos Alberto Torres Naira,et al.  Classification of People who Suffer Schizophrenia and Healthy People by EEG Signals using Deep Learning , 2019, International Journal of Advanced Computer Science and Applications.

[81]  Ling Shao,et al.  Transfer Learning for Visual Categorization: A Survey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[82]  Lijuan Duan,et al.  Motor Imagery EEG Classification Based on Kernel Hierarchical Extreme Learning Machine , 2017, Cognitive Computation.

[83]  Wei Wu,et al.  Deep learning based on Batch Normalization for P300 signal detection , 2018, Neurocomputing.

[84]  Maysam F. Abbod,et al.  Spectrum Analysis of EEG Signals Using CNN to Model Patient’s Consciousness Level Based on Anesthesiologists’ Experience , 2019, IEEE Access.

[85]  Saraju P. Mohanty,et al.  Neuro-Detect: A Machine Learning-Based Fast and Accurate Seizure Detection System in the IoMT , 2019, IEEE Transactions on Consumer Electronics.

[86]  J. Buhmann,et al.  Automatic Human Sleep Stage Scoring Using Deep Neural Networks , 2018, Front. Neurosci..

[87]  Pasin Israsena,et al.  EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation , 2014, TheScientificWorldJournal.

[88]  Yong Peng,et al.  EEG-based emotion classification using deep belief networks , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[89]  Jiankun Hu,et al.  Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States , 2019, IEEE Transactions on Information Forensics and Security.

[90]  Wolfram Burgard,et al.  Deep learning with convolutional neural networks for EEG decoding and visualization , 2017, Human brain mapping.

[91]  Kyriaki-Margarita Bintsi,et al.  Bullying incidences identification within an immersive environment using HD EEG-based analysis: A Swarm Decomposition and Deep Learning approach , 2017, Scientific Reports.

[92]  Ye Wang,et al.  Adversarial Deep Learning in EEG Biometrics , 2019, IEEE Signal Processing Letters.

[93]  Feng Duan,et al.  A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals , 2019, IEEE Access.

[94]  Petia D. Koprinkova-Hristova,et al.  Reservoir computing for emotion valence discrimination from EEG signals , 2017, Neurocomputing.

[95]  Shuai Wang,et al.  EEG Classification of Motor Imagery Using a Novel Deep Learning Framework , 2019, Sensors.

[96]  Hubert Cecotti,et al.  Convolutional Neural Network with embedded Fourier Transform for EEG classification , 2008, 2008 19th International Conference on Pattern Recognition.

[97]  Jianhua Zhang,et al.  A deep learning scheme for mental workload classification based on restricted Boltzmann machines , 2017, Cognition, Technology & Work.

[98]  Khaled A. Harras,et al.  A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems , 2018, IEEE Access.

[99]  Hanadi Hassen,et al.  ROM-based Inference Method Built on Deep Learning for Sleep Stage Classification , 2019 .

[100]  Hong Wang,et al.  A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection , 2019, Int. J. Neural Syst..

[101]  Wei Wei,et al.  Deep Channel-Correlation Network for Motor Imagery Decoding From the Same Limb , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[102]  Xiaojun Bi,et al.  Early Alzheimer's disease diagnosis based on EEG spectral images using deep learning , 2019, Neural Networks.

[103]  Mehmet Siraç Özerdem,et al.  Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals , 2019, Brain Science.

[104]  Dario Pompili,et al.  Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things , 2017, IEEE Transactions on Big Data.

[105]  Filippo Zappasodi,et al.  Deep Convolutional Neural Networks for Feature-Less Automatic Classification of Independent Components in Multi-Channel Electrophysiological Brain Recordings , 2019, IEEE Transactions on Biomedical Engineering.

[106]  Yan Song,et al.  Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD , 2019, Journal of neural engineering.

[107]  Aiguo Song,et al.  Wavelet Transform Time-Frequency Image and Convolutional Network-Based Motor Imagery EEG Classification , 2019, IEEE Access.

[108]  Mingyang Li,et al.  Decoding human brain activity with deep learning , 2020, Biomed. Signal Process. Control..

[109]  Ulas Baran Baloglu,et al.  CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION , 2019, Journal of Mechanics in Medicine and Biology.

[110]  Gea Drost,et al.  Outcome Prediction in Postanoxic Coma With Deep Learning. , 2019, Critical care medicine.

[111]  L. F. Haas.  Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography , 2003, Journal of neurology, neurosurgery, and psychiatry.

[112]  Hang-Bong Kang,et al.  An analysis of smartphone overuse recognition in terms of emotions using brainwaves and deep learning , 2018, Neurocomputing.

[113]  Ju Lynn Ong,et al.  An end-to-end framework for real-time automatic sleep stage classification , 2018, Sleep.

[114]  Liang Dong,et al.  Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework , 2018, Comput. Intell. Neurosci..

[115]  Weidong Zhou,et al.  Denoising Sparse Autoencoder-Based Ictal EEG Classification , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[116]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[117]  Leonardo Mostarda,et al.  Artificial Muscle Intelligence System With Deep Learning for Post-Stroke Assistance and Rehabilitation , 2019, IEEE Access.

[118]  Mark R. Bower,et al.  Microseizures and the spatiotemporal scales of human partial epilepsy. , 2010, Brain : a journal of neurology.

[119]  Vili Podgorelec,et al.  Automatic Classification of Motor Impairment Neural Disorders from EEG Signals Using Deep Convolutional Neural Networks , 2018, Elektronika ir Elektrotechnika.

[120]  U. Rajendra Acharya,et al.  Automated EEG-based screening of depression using deep convolutional neural network , 2018, Comput. Methods Programs Biomed..

[121]  B. Blankertz,et al.  A New Auditory Multi-Class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue , 2010, PloS one.

[122]  K. Maghooli,et al.  Emotion Recognition from Physiological Signals Using Parallel Stacked Autoencoders , 2019, Neurophysiology.

[123]  Yi Zhou,et al.  Automatic seizure detection using three-dimensional CNN based on multi-channel EEG , 2018, BMC Medical Informatics and Decision Making.

[124]  Gordon Cheng,et al.  Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals , 2018, Sensors.

[125]  Jing Zhu,et al.  Multimodal Mild Depression Recognition Based on EEG-EM Synchronization Acquisition Network , 2019, IEEE Access.

[126]  Christoph Reichert,et al.  Convolutional Neural Networks for Decoding of Covert Attention Focus and Saliency Maps for EEG Feature Visualization , 2019 .

[127]  Benjamin Blankertz,et al.  Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP) , 2013, Clinical Neurophysiology.

[128]  Wendong Ge,et al.  Automated tracking of level of consciousness and delirium in critical illness using deep learning , 2019, npj Digital Medicine.

[129]  Florian Chapotot,et al.  Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules , 2009 .

[130]  Alexandra Piryatinska,et al.  Binary classification of multichannel-EEG records based on the ϵ-complexity of continuous vector functions , 2017, Comput. Methods Programs Biomed..

[131]  Junming Zhang,et al.  Complex-valued unsupervised convolutional neural networks for sleep stage classification , 2018, Comput. Methods Programs Biomed..

[132]  Hojjat Adeli,et al.  A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals , 2019, Expert Syst. J. Knowl. Eng..

[133]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[134]  Wei You,et al.  Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[135]  Nikola Kasabov,et al.  Modelling Peri-Perceptual Brain Processes in a Deep Learning Spiking Neural Network Architecture , 2018, Scientific Reports.

[136]  Magdy Bayoumi,et al.  Semi-Supervised EEG Signals Classification System for Epileptic Seizure Detection , 2019, IEEE Signal Processing Letters.

[137]  Tzyy-Ping Jung,et al.  Combining ERPs and EEG spectral features for decoding intended movement direction , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[138]  Wenhua Cui,et al.  Feature recognition of motor imaging EEG signals based on deep learning , 2019, Personal and Ubiquitous Computing.

[139]  Saeid Sanei,et al.  Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[140]  Ran Manor,et al.  Convolutional Neural Network for Multi-Category Rapid Serial Visual Presentation BCI , 2015, Front. Comput. Neurosci..

[141]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[142]  Jungjoon Kim,et al.  Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease , 2018, International journal of environmental research and public health.

[143]  Erik Bresch,et al.  Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG , 2018, Front. Comput. Neurosci..

[144]  Dongrui Wu,et al.  Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[145]  Hyun-Soo Choi,et al.  Pediatric Sleep Stage Classification Using Multi-Domain Hybrid Neural Networks , 2019, IEEE Access.

[146]  Michael Stonebraker,et al.  Smile: A System to Support Machine Learning on EEG Data at Scale , 2019, Proc. VLDB Endow..

[147]  Bahar Khalighinejad,et al.  Towards reconstructing intelligible speech from the human auditory cortex , 2019, Scientific Reports.

[148]  Andrzej Cichocki,et al.  Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA , 2020, Neurocomputing.

[149]  Andrzej Cichocki,et al.  EmotionMeter: A Multimodal Framework for Recognizing Human Emotions , 2019, IEEE Transactions on Cybernetics.

[150]  Francesco Carlo Morabito,et al.  A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings , 2019, Neurocomputing.

[151]  Guojun Dai,et al.  EEG classification of driver mental states by deep learning , 2018, Cognitive Neurodynamics.

[152]  U Rajendra Acharya,et al.  A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals , 2019, International journal of environmental research and public health.

[153]  Hao Dong,et al.  Mixed Neural Network Approach for Temporal Sleep Stage Classification , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[154]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[155]  Hilal Kaya,et al.  A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing , 2019, Biocybernetics and Biomedical Engineering.

[156]  Hao Wu,et al.  A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification , 2019, Front. Neurosci..

[157]  Anthony J. Ries,et al.  Decoding P300 Variability Using Convolutional Neural Networks , 2019, bioRxiv.

[158]  Mohammad I. Daoud,et al.  A Deep Learning Framework for Decoding Motor Imagery Tasks of the Same Hand Using EEG Signals , 2019, IEEE Access.

[159]  Peng Xu,et al.  The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing , 2017, Journal of Neuroscience Methods.

[160]  Saeid Sanei,et al.  Deep Neural Architectures for Mapping Scalp to Intracranial EEG , 2018, Int. J. Neural Syst..

[161]  M S Treder,et al.  Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification , 2014, Journal of neural engineering.

[162]  Rajarathnam Chandramouli,et al.  Decoding Asynchronous Reaching in Electroencephalography Using Stacked Autoencoders , 2018, IEEE Access.

[163]  Muhammad Ghulam,et al.  Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion , 2019, Future Gener. Comput. Syst..

[164]  Maryam Gholami Doborjeh,et al.  A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects , 2016, IEEE Transactions on Biomedical Engineering.

[165]  Wan-Young Chung,et al.  A Single-Channel SSVEP-Based BCI Speller Using Deep Learning , 2019, IEEE Access.

[166]  Wajid Mumtaz,et al.  A deep learning framework for automatic diagnosis of unipolar depression , 2019, Int. J. Medical Informatics.

[167]  Bin Chen,et al.  Driving Fatigue Detection from EEG Using a Modified PCANet Method , 2019, Comput. Intell. Neurosci..

[168]  Z. Mousavi,et al.  Deep convolutional neural network for classification of sleep stages from single-channel EEG signals , 2019, Journal of Neuroscience Methods.

[169]  Zhong Yin,et al.  Cross-subject recognition of operator functional states via EEG and switching deep belief networks with adaptive weights , 2017, Neurocomputing.

[170]  Zenghui Wang,et al.  Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.

[171]  Ganesh R. Naik,et al.  Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks , 2017, Front. Neurosci..

[172]  Jin-Woo Jeong,et al.  Motor Imagery EEG Classification Using Capsule Networks† , 2019, Sensors.

[173]  Ahmed Fares,et al.  EEG-based image classification via a region-level stacked bi-directional deep learning framework , 2019, BMC Medical Informatics Decis. Mak..

[174]  T. Young,et al.  Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. , 2008, Sleep.

[175]  Junfeng Chen,et al.  Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[176]  Rohan Akut,et al.  Wavelet based deep learning approach for epilepsy detection , 2019, Health Information Science and Systems.

[177]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[178]  Zhaoxiang Zhang,et al.  Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition , 2017, Cognitive Computation.

[179]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[180]  Mounya Elhilali,et al.  Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals , 2018, Front. Neurosci..

[181]  Paul Sajda,et al.  Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials , 2018, Journal of neural engineering.

[182]  Shin-Dug Kim,et al.  Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System , 2018, Sensors.

[183]  Jinfeng Gao,et al.  Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG , 2020, Comput. Methods Programs Biomed..

[184]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[185]  Liang Dong,et al.  Emotion Recognition from Multiband EEG Signals Using CapsNet , 2019, Sensors.

[186]  Dae-Hyeok Lee,et al.  Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals , 2019, Brain sciences.

[187]  Jiaming Zhang,et al.  EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model , 2019, Brain sciences.

[188]  Mohammad Reza Keyvanpour,et al.  A new method for P300 detection in deep belief networks: Nesterov momentum and drop based learning rate , 2018, Health and Technology.

[189]  Anikó Ekárt,et al.  A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction , 2019, Complex..

[190]  Chin-Teng Lin,et al.  Subject adaptation network for EEG data analysis , 2019, Appl. Soft Comput..

[191]  Kazuko Hayashi,et al.  Hierarchical Poincaré analysis for anaesthesia monitoring , 2019, Journal of Clinical Monitoring and Computing.

[192]  Tingxi Wen,et al.  Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals , 2018, IEEE Access.

[193]  Ghanahshyam B Kshirsagar,et al.  Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning , 2019, IEEE Transactions on Biomedical Engineering.

[194]  J. X. Chen,et al.  A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification , 2019, IEEE Access.

[195]  David Kent,et al.  Automated Sleep Stage Scoring of the Sleep Heart Health Study Using Deep Neural Networks. , 2019, Sleep.

[196]  Lei Cao,et al.  Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition , 2018, Concurr. Comput. Pract. Exp..

[197]  Fuqiang Chen,et al.  Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers , 2016 .

[198]  U. Rajendra Acharya,et al.  Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals , 2017, Comput. Biol. Medicine.

[199]  Zong Qun,et al.  A novel hybrid deep learning scheme for four-class motor imagery classification , 2019, Journal of neural engineering.

[200]  Andrzej Cichocki,et al.  Multikernel Capsule Network for Schizophrenia Identification , 2019, IEEE Transactions on Cybernetics.

[201]  Akshay Arora,et al.  Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings , 2018, Journal of neural engineering.

[202]  M. Shamim Hossain,et al.  Cognitive Smart Healthcare for Pathology Detection and Monitoring , 2019, IEEE Access.

[203]  Jian Zhang,et al.  Deep Extreme Learning Machine and Its Application in EEG Classification , 2015 .

[204]  Lukáš Vařeka,et al.  Stacked Autoencoders for the P300 Component Detection , 2017, Front. Neurosci..

[205]  Gabriela Castellano,et al.  A correntropy-based classifier for motor imagery brain-computer interfaces , 2019, Biomedical Physics & Engineering Express.

[206]  Zhong Yin,et al.  Recognition of emotions using multimodal physiological signals and an ensemble deep learning model , 2017, Comput. Methods Programs Biomed..

[207]  Andrzej Cichocki,et al.  Deep Learning of Multifractal Attributes from Motor Imagery Induced EEG , 2014, ICONIP.

[208]  Tong Zhang,et al.  Spatial–Temporal Recurrent Neural Network for Emotion Recognition , 2017, IEEE Transactions on Cybernetics.

[209]  Magdy Bayoumi,et al.  Efficient Epileptic Seizure Prediction Based on Deep Learning , 2019, IEEE Transactions on Biomedical Circuits and Systems.

[210]  A. Varri,et al.  The SIESTA project polygraphic and clinical database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[211]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[212]  Ran Manor,et al.  Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface , 2016, Front. Comput. Neurosci..

[213]  Jianmin Jiang,et al.  A Context-Supported Deep Learning Framework for Multimodal Brain Imaging Classification , 2019, IEEE Transactions on Human-Machine Systems.

[214]  Justin A. Blanco,et al.  Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement , 2011, Journal of neural engineering.

[215]  Shiyu Yi,et al.  Small-Scale Demographic Sequences Projection Based on Time Series Clustering and LSTM-RNN , 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

[216]  Mathias Baumert,et al.  Automatic A-Phase Detection of Cyclic Alternating Patterns in Sleep Using Dynamic Temporal Information , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[217]  Jinxing Lin,et al.  Pilots’ Fatigue Status Recognition Using Deep Contractive Autoencoder Network , 2019, IEEE Transactions on Instrumentation and Measurement.

[218]  James C. Christensen,et al.  Cross-Participant EEG-Based Assessment of Cognitive Workload Using Multi-Path Convolutional Recurrent Neural Networks , 2018, Sensors.

[219]  Khaled Shaalan,et al.  Speech Recognition Using Deep Neural Networks: A Systematic Review , 2019, IEEE Access.

[220]  Na Lu,et al.  A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[221]  Yufei Huang,et al.  Feature Selection of Deep Learning Models for EEG-Based RSVP Target Detection , 2019, IEICE Trans. Inf. Syst..

[222]  Yu-Te Wang,et al.  A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials , 2015, PloS one.

[223]  Jie Hu,et al.  Simultaneous Human Health Monitoring and Time-Frequency Sparse Representation Using EEG and ECG Signals , 2019, IEEE Access.

[224]  Ruoyu Du,et al.  Cross-Subject EEG Signal Recognition Using Deep Domain Adaptation Network , 2019, IEEE Access.

[225]  D. Jude Hemanth,et al.  Brain signal based human emotion analysis by circular back propagation and Deep Kohonen Neural Networks , 2018, Comput. Electr. Eng..

[226]  Rubén San-Segundo-Hernández,et al.  Classification of epileptic EEG recordings using signal transforms and convolutional neural networks , 2019, Comput. Biol. Medicine.

[227]  Rabab Kreidieh Ward,et al.  Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals , 2017, IEEE Transactions on Biomedical Engineering.

[228]  Xiang Li,et al.  Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring , 2017, Int. J. Data Min. Bioinform..

[229]  Mohamed Elhoseny,et al.  Emotion recognition using empirical mode decomposition and approximation entropy , 2018, Comput. Electr. Eng..

[230]  Min Liu,et al.  A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification , 2019, IEEE Access.

[231]  Jiang Li,et al.  Deep Models for Engagement Assessment With Scarce Label Information , 2016, IEEE Transactions on Human-Machine Systems.

[232]  Yongtian He,et al.  Deep learning for electroencephalogram (EEG) classification tasks: a review , 2019, Journal of neural engineering.

[233]  Ekapol Chuangsuwanich,et al.  Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder , 2018, IEEE Access.

[234]  Debi Prosad Dogra,et al.  EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model , 2019, IEEE Sensors Journal.

[235]  Francesco Carlo Morabito,et al.  Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia , 2017, Int. J. Neural Syst..

[236]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[237]  Dezhong Yao,et al.  Separated channel convolutional neural network to realize the training free motor imagery BCI systems , 2019, Biomed. Signal Process. Control..

[238]  U. Rajendra Acharya,et al.  Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals , 2019, Applied Sciences.

[239]  Fan Xiong,et al.  Epileptic seizure detection and prediction using stacked bidirectional long short term memory , 2019, Pattern Recognit. Lett..

[240]  Panteleimon Chriskos,et al.  Automatic Sleep Staging Employing Convolutional Neural Networks and Cortical Connectivity Images , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[241]  Stanislas Chambon,et al.  A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.