Deep learning for EEG data analytics: A survey

In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi‐supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi‐supervised learning on EEG data analytics and discussing the importance of semi‐supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.

[1]  Fei Wang,et al.  Predicting Seizures from Electroencephalography Recordings: A Knowledge Transfer Strategy , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[2]  Mohammad Soleymani,et al.  Continuous emotion detection using EEG signals and facial expressions , 2014, 2014 IEEE International Conference on Multimedia and Expo (ICME).

[3]  Suprava Patnaik,et al.  Deep RNN learning for EEG based functional brain state inference , 2017, 2017 International Conference on Advances in Computing, Communication and Control (ICAC3).

[4]  Brent Lance,et al.  EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces , 2016, Journal of neural engineering.

[5]  Giulio Ruffini,et al.  Deep learning using EEG spectrograms for prognosis in idiopathic rapid eye movement behavior disorder (RBD) , 2018 .

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

[7]  Joelle Pineau,et al.  Learning Robust Features using Deep Learning for Automatic Seizure Detection , 2016, MLHC.

[8]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Saeid Sanei,et al.  Deep learning for epileptic intracranial EEG data , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[10]  Heesung Kwon,et al.  Single-trial EEG RSVP classification using convolutional neural networks , 2016, Defense + Security.

[11]  Lianghua He,et al.  Deep Learning in the EEG Diagnosis of Alzheimer's Disease , 2014, ACCV Workshops.

[12]  Fang-Yie Leu,et al.  An MFCC‐based text‐independent speaker identification system for access control , 2018, Concurr. Comput. Pract. Exp..

[13]  Yoshua Bengio,et al.  Attention-Based Models for Speech Recognition , 2015, NIPS.

[14]  Ana Carolina Lorena,et al.  Improving Alzheimer's Disease Diagnosis with Machine Learning Techniques , 2011, Clinical EEG and neuroscience.

[15]  Lei Xie,et al.  Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks , 2017, BCB.

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

[17]  Richard D. Jones,et al.  EEG-Based Lapse Detection With High Temporal Resolution , 2007, IEEE Transactions on Biomedical Engineering.

[18]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[19]  Hubert Cecotti,et al.  Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Byunghan Lee,et al.  Deep learning in bioinformatics , 2016, Briefings Bioinform..

[21]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Klaus-Robert Müller,et al.  Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring , 2008, Journal of Neuroscience Methods.

[23]  Yi Zheng,et al.  Weakly-Supervised Deep Learning for Customer Review Sentiment Classification , 2016, IJCAI.

[24]  Klaus Linkenkaer-Hansen,et al.  EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease , 2017, Scientific Reports.

[25]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[26]  Hong Yu,et al.  Semi-Supervised Clustering for Vigilance Analysis Based on EEG , 2007, 2007 International Joint Conference on Neural Networks.

[27]  Surya Ganguli,et al.  Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.

[28]  Mohammed Yeasin,et al.  Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks , 2015, ICLR.

[29]  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.

[30]  Alexandros T. Tzallas,et al.  Epileptic Seizures Classification Based on Long-Term EEG Signal Wavelet Analysis , 2017, BHI 2017.

[31]  Yann LeCun,et al.  Classification of patterns of EEG synchronization for seizure prediction , 2009, Clinical Neurophysiology.

[32]  Aidong Zhang,et al.  A Novel Semi-Supervised Deep Learning Framework for Affective State Recognition on EEG Signals , 2014, 2014 IEEE International Conference on Bioinformatics and Bioengineering.

[33]  Charles W. Anderson,et al.  Classification of EEG during imagined mental tasks by forecasting with Elman Recurrent Neural Networks , 2011, The 2011 International Joint Conference on Neural Networks.

[34]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

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

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

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

[38]  Homayoun Mahdavi-Nasab,et al.  Analysis and classification of EEG signals using spectral analysis and recurrent neural networks , 2010, 2010 17th Iranian Conference of Biomedical Engineering (ICBME).

[39]  Shouqian Sun,et al.  Single-trial EEG classification of motor imagery using deep convolutional neural networks , 2017 .

[40]  M. Murugappan,et al.  Wireless EEG signals based Neuromarketing system using Fast Fourier Transform (FFT) , 2014, 2014 IEEE 10th International Colloquium on Signal Processing and its Applications.

[41]  Yuanqing Li,et al.  A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system , 2008, Pattern Recognit. Lett..

[42]  Michael E. Smith,et al.  Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods , 1998, Hum. Factors.

[43]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[45]  Gang Pan,et al.  Remembered or Forgotten?—An EEG-Based Computational Prediction Approach , 2016, PloS one.

[46]  Shlomo Bentin,et al.  Motor and attentional mechanisms involved in social interaction—Evidence from mu and alpha EEG suppression , 2011, NeuroImage.

[47]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[48]  Debi Prosad Dogra,et al.  Analysis of EEG signals and its application to neuromarketing , 2017, Multimedia Tools and Applications.

[49]  Yufei Huang,et al.  Driver's fatigue prediction by deep covariance learning from EEG , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[50]  Samy Bengio,et al.  HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems , 2004, ESANN.

[51]  Michael E. Smith,et al.  Monitoring Task Loading with Multivariate EEG Measures during Complex Forms of Human-Computer Interaction , 2001, Hum. Factors.

[52]  Ya Zhang,et al.  Chinese Typeface Transformation with Hierarchical Adversarial Network , 2017, ArXiv.

[53]  Shuicheng Yan,et al.  Parallel convolutional-linear neural network for motor imagery classification , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[54]  Tinoosh Mohsenin,et al.  Wearable seizure detection using convolutional neural networks with transfer learning , 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

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

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

[57]  Qi Wang,et al.  Deep convolutional neural network for drowsy student state detection , 2018, Concurr. Comput. Pract. Exp..

[58]  Peyman Goli,et al.  Early Assessment of Mild Alzheimer’s Disease Using Elman Neural Network, LDA and SVM Methods , 2017 .

[59]  Lianghua He,et al.  A Deep Learning Method for Classification of EEG Data Based on Motor Imagery , 2014, ICIC.

[60]  Bao-Liang Lu,et al.  Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network , 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

[61]  Brian Litt,et al.  Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[62]  Jorge Nocedal,et al.  Optimization Methods for Large-Scale Machine Learning , 2016, SIAM Rev..

[63]  Amy Loutfi,et al.  Sleep Stage Classification Using Unsupervised Feature Learning , 2012, Adv. Artif. Neural Syst..

[64]  Bao-Liang Lu,et al.  Emotional state classification from EEG data using machine learning approach , 2014, Neurocomputing.

[65]  Tzyy-Ping Jung,et al.  Feature extraction with deep belief networks for driver's cognitive states prediction from EEG data , 2015, 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP).

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

[67]  Donald C. Wunsch,et al.  Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG , 2000, Neurocomputing.

[68]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[69]  Joseph Picone,et al.  Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures , 2017, Front. Hum. Neurosci..

[70]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[71]  J. T. Turner,et al.  Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection , 2017, AAAI Spring Symposia.

[72]  R. Harner,et al.  Patient-Specific Early Seizure Detection From Scalp Electroencephalogram , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[73]  Christoph Lehmann,et al.  Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG) , 2007, Journal of Neuroscience Methods.

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

[75]  Yan Wu,et al.  Convolutional deep belief networks for feature extraction of EEG signal , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[76]  Sebastian Stober,et al.  Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings , 2014, NIPS.

[77]  Ali H. Shoeb,et al.  Application of Machine Learning To Epileptic Seizure Detection , 2010, ICML.

[78]  J. Crowcroft,et al.  Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine , 2012, Journal of Neuroscience Methods.

[79]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.