A review on transfer learning in EEG signal analysis
暂无分享,去创建一个
Xiaohui Liu | Zitong Wan | Rui Yang | Nianyin Zeng | Mengjie Huang | Zitong Wan | Rui Yang | Mengjie Huang | Nianyin Zeng | Xiaohui Liu
[1] Terrence J. Sejnowski,et al. Utilizing Deep Learning Towards Multi-Modal Bio-Sensing and Vision-Based Affective Computing , 2019, IEEE Transactions on Affective Computing.
[2] Dongrui Wu,et al. EEG-Based Driver Drowsiness Estimation Using an Online Multi-View and Transfer TSK Fuzzy System , 2021, IEEE Transactions on Intelligent Transportation Systems.
[3] Zidong Wang,et al. A Hybrid Model- and Memory-Based Collaborative Filtering Algorithm for Baseline Data Prediction of Friedreich's Ataxia Patients , 2021, IEEE Transactions on Industrial Informatics.
[4] Kaijian Xia,et al. Cross-Domain Classification Model With Knowledge Utilization Maximization for Recognition of Epileptic EEG Signals , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[5] Yuan Yuan,et al. A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer , 2019, IEEE Transactions on Cybernetics.
[6] Bao-Liang Lu,et al. Driver sleepiness detection from EEG and EOG signals using GAN and LSTM networks , 2020, Neurocomputing.
[7] Cheng Liu,et al. A machine emotion transfer model for intelligent human-machine interaction based on group division , 2020 .
[8] Domenico Tegolo,et al. Graph-theoretical derivation of brain structural connectivity , 2020, Appl. Math. Comput..
[9] Huiguang He,et al. Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition , 2020, IEEE Transactions on Cybernetics.
[10] Wei Liu,et al. A MultiKernel Domain Adaptation Method for Unsupervised Transfer Learning on Cross-Source and Cross-Region Remote Sensing Data Classification , 2020, IEEE Transactions on Geoscience and Remote Sensing.
[11] Changde Du,et al. Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity , 2020, IEEE Transactions on Cognitive and Developmental Systems.
[12] Sahar Moghimi,et al. EEG signal classification of imagined speech based on Riemannian distance of correntropy spectral density , 2020, Biomed. Signal Process. Control..
[13] Yuan-Pin Lin,et al. Constructing a Personalized Cross-Day EEG-Based Emotion-Classification Model Using Transfer Learning , 2020, IEEE Journal of Biomedical and Health Informatics.
[14] Murat Akcakaya,et al. A probabilistic approach for calibration time reduction in hybrid EEG–fTCD brain–computer interfaces , 2020, BioMedical Engineering OnLine.
[15] Chun-Shu Wei,et al. Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses , 2020, IEEE Transactions on Biomedical Engineering.
[16] Fei Wang,et al. Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier , 2020, Applied Sciences.
[17] Ye Wang,et al. Learning Invariant Representations From EEG via Adversarial Inference , 2020, IEEE Access.
[18] Samit Ari,et al. MsCNN: A Deep Learning Framework for P300-Based Brain–Computer Interface Speller , 2020, IEEE Transactions on Medical Robotics and Bionics.
[19] Natarajan Sriraam,et al. EEG based multi-class seizure type classification using convolutional neural network and transfer learning , 2020, Neural Networks.
[20] Xianrui Zhang,et al. Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding , 2020, Entropy.
[21] Mohd Ibrahim Shapiai,et al. Transfer Learning of BCI Using CUR Algorithm , 2020, J. Signal Process. Syst..
[22] He He,et al. Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[23] Dongrui Wu,et al. Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[24] Dongrui Wu,et al. Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach , 2018, IEEE Transactions on Biomedical Engineering.
[25] Debabrata Datta,et al. Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network , 2020, Biocybernetics and Biomedical Engineering.
[26] Mahnaz Arvaneh,et al. Dynamic time warping-based transfer learning for improving common spatial patterns in brain–computer interface , 2019, Journal of neural engineering.
[27] Anup Nandy,et al. Data Augmentation for Ambulatory EEG Based Cognitive State Taxonomy System with RNN-LSTM , 2019, SGAI Conf..
[28] Jong-Myon Kim,et al. A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals , 2019, Brain sciences.
[29] Hao Wu,et al. A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification , 2019, Front. Neurosci..
[30] Chin-Teng Lin,et al. Subject adaptation network for EEG data analysis , 2019, Appl. Soft Comput..
[31] Andrzej Cichocki,et al. Novel hybrid brain–computer interface system based on motor imagery and P300 , 2019, Cognitive Neurodynamics.
[32] Oren Shriki,et al. Co-adaptive Training Improves Efficacy of a Multi-Day EEG-Based Motor Imagery BCI Training , 2019, Front. Hum. Neurosci..
[33] Mengjie Zhang,et al. GP-based methods for domain adaptation: using brain decoding across subjects as a test-case , 2019, Genetic Programming and Evolvable Machines.
[34] Jie Zhang,et al. Transductive Transfer Learning-Based Spectrum Optimization for Resource Reservation in Seven-Core Elastic Optical Networks , 2019, Journal of Lightwave Technology.
[35] Inga Wang,et al. A performance based feature selection technique for subject independent MI based BCI , 2019, Health Information Science and Systems.
[36] Christian Jutten,et al. Riemannian Procrustes Analysis: Transfer Learning for Brain–Computer Interfaces , 2019, IEEE Transactions on Biomedical Engineering.
[37] Min Liu,et al. A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification , 2019, IEEE Access.
[38] U. Rajendra Acharya,et al. Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals , 2019, Applied Sciences.
[39] Magdy Bayoumi,et al. Efficient Epileptic Seizure Prediction Based on Deep Learning , 2019, IEEE Transactions on Biomedical Circuits and Systems.
[40] Jianhua Zhang,et al. Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework , 2019, Neurocomputing.
[41] Danbing Du,et al. Experimental Study on Neural Feedback in Embedded System Teaching Processing Based on ERP Signal Analysis , 2019, Int. J. Emerg. Technol. Learn..
[42] Hua Zhang,et al. Transfer Learning Based on Regularized Common Spatial Patterns Using Cosine Similarities of Spatial Filters for Motor-Imagery BCI , 2019, J. Circuits Syst. Comput..
[43] Mahnaz Arvaneh,et al. Weighted Transfer Learning for Improving Motor Imagery-Based Brain–Computer Interface , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[44] Zhaohong Deng,et al. Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals , 2019, IEEE Transactions on Cybernetics.
[45] Dezhi Zheng,et al. Domain Transfer Multiple Kernel Boosting for Classification of EEG Motor Imagery Signals , 2019, IEEE Access.
[46] Luis Villaseñor Pineda,et al. Transfer learning in imagined speech EEG-based BCIs , 2019, Biomed. Signal Process. Control..
[47] Zhibin Zhao,et al. Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing , 2019, IEEE Transactions on Industrial Informatics.
[48] Shitong Wang,et al. Recognition of Multiclass Epileptic EEG Signals Based on Knowledge and Label Space Inductive Transfer , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[49] Gernot R. Müller-Putz,et al. Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets , 2019, IEEE Transactions on Cognitive and Developmental Systems.
[50] Rainer Goebel,et al. Transfer learning of deep neural network representations for fMRI decoding , 2019, Journal of Neuroscience Methods.
[51] Shuai Wang,et al. EEG Classification of Motor Imagery Using a Novel Deep Learning Framework , 2019, Sensors.
[52] Murat Akcakaya,et al. Transfer Learning for a Multimodal Hybrid EEG-fTCD Brain–Computer Interface , 2019, IEEE Sensors Letters.
[53] Cuntai Guan,et al. Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI , 2019, Journal of neural engineering.
[54] Yiqiang Chen,et al. Cross-position Activity Recognition with Stratified Transfer Learning , 2018, Pervasive Mob. Comput..
[55] Girijesh Prasad,et al. Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface , 2018, Neurocomputing.
[56] Sadasivan Puthusserypady,et al. An end-to-end deep learning approach to MI-EEG signal classification for BCIs , 2018, Expert Syst. Appl..
[57] Rodrigo Ramele,et al. EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces , 2018, Brain sciences.
[58] Bin Deng,et al. Reconstruction of functional brain network in Alzheimer's disease via cross-frequency phase synchronization , 2018, Neurocomputing.
[59] Petr Klimes,et al. Intracerebral EEG Artifact Identification Using Convolutional Neural Networks , 2018, Neuroinformatics.
[60] Christian Jutten,et al. Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces , 2018, IEEE Transactions on Biomedical Engineering.
[61] Hang Chang,et al. Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[62] Ou Bai,et al. Multi-subject subspace alignment for non-stationary EEG-based emotion recognition , 2018, Technology and health care : official journal of the European Society for Engineering and Medicine.
[63] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update , 2018, Journal of neural engineering.
[64] Fuchun Sun,et al. Deep Transfer Learning for EEG-Based Brain Computer Interface , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[65] Klaus-Robert Müller,et al. Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier] , 2018, IEEE Computational Intelligence Magazine.
[66] Panagiotis Artemiadis,et al. Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features , 2018, Journal of neural engineering.
[67] Panagiotis K. Artemiadis,et al. EEG feature descriptors and discriminant analysis under Riemannian Manifold perspective , 2018, Neurocomputing.
[68] Lorena R. R. Gianotti,et al. Theta resting EEG in TPJ/pSTS is associated with individual differences in the feeling of being looked at , 2017, Social cognitive and affective neuroscience.
[69] David Zhang,et al. Learning Domain-Invariant Subspace Using Domain Features and Independence Maximization , 2016, IEEE Transactions on Cybernetics.
[70] Shiliang Sun,et al. Domain Adaptation with Twin Support Vector Machines , 2017, Neural Processing Letters.
[71] Eduardo Rocon,et al. Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing , 2017, Sensors.
[72] Lauren Reinerman-Jones,et al. Metrics for individual differences in EEG response to cognitive workload: Optimizing performance prediction , 2017 .
[73] Pengjiang Qian,et al. Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[74] Tao Zhang,et al. LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[75] Jianhua Zhang,et al. Cross-subject mental workload classification using kernel spectral regression and transfer learning techniques , 2017, Cognition, Technology & Work.
[76] Jianhua Zhang,et al. Pattern Classification of Instantaneous Cognitive Task-load Through GMM Clustering, Laplacian Eigenmap, and Ensemble SVMs , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[77] Qingyao Wu,et al. Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources , 2017, IEEE Transactions on Knowledge and Data Engineering.
[78] T. Jung,et al. Improving EEG-Based Emotion Classification Using Conditional Transfer Learning , 2017, Front. Hum. Neurosci..
[79] Kiseon Kim,et al. Multiple kernel learning based on three discriminant features for a P300 speller BCI , 2017, Neurocomputing.
[80] Amit Konar,et al. A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection , 2017, Front. Neurosci..
[81] Dan Liu,et al. A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition , 2017, Sensors.
[82] Yurong Liu,et al. A survey of deep neural network architectures and their applications , 2017, Neurocomputing.
[83] Alexandre Barachant,et al. Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review , 2017 .
[84] Brent Lance,et al. Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR) , 2017, IEEE Transactions on Fuzzy Systems.
[85] Athanasios V. Vasilakos,et al. Brain computer interface: control signals review , 2017, Neurocomputing.
[86] Martin Wattenberg,et al. Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation , 2016, TACL.
[87] Fabrizio de Vico Fallani,et al. Riemannian Geometry Applied to Detection of Respiratory States From EEG Signals: The Basis for a Brain–Ventilator Interface , 2016, IEEE Transactions on Biomedical Engineering.
[88] Haixian Wang,et al. Regularized common spatial patterns with subject-to-subject transfer of EEG signals , 2017, Cognitive Neurodynamics.
[89] Qisong Wang,et al. Unsupervised domain adaptation techniques based on auto-encoder for non-stationary EEG-based emotion recognition , 2016, Comput. Biol. Medicine.
[90] Ming-Ai Li,et al. Applying a Locally Linear Embedding Algorithm for Feature Extraction and Visualization of MI-EEG , 2016, J. Sensors.
[91] Zhaohong Deng,et al. Enhanced Knowledge-Leverage-Based TSK Fuzzy System Modeling for Inductive Transfer Learning , 2016, ACM Trans. Intell. Syst. Technol..
[92] Taghi M. Khoshgoftaar,et al. A survey of transfer learning , 2016, Journal of Big Data.
[93] Chuang Lin,et al. Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[94] S Bonnet,et al. Efficient mental workload estimation using task-independent EEG features , 2016, Journal of neural engineering.
[95] Yong Peng,et al. Discriminative manifold extreme learning machine and applications to image and EEG signal classification , 2016, Neurocomputing.
[96] Jérémy Frey,et al. Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort , 2015, Comput. Intell. Neurosci..
[97] Sung Chan Jun,et al. Increasing session-to-session transfer in a brain–computer interface with on-site background noise acquisition , 2015, Journal of neural engineering.
[98] Xiaofeng Gong,et al. Tensor decomposition of EEG signals: A brief review , 2015, Journal of Neuroscience Methods.
[99] K. Newell,et al. Modulation of cortical activity in 2D versus 3D virtual reality environments: an EEG study. , 2015, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[100] Bin Li,et al. Online Transfer Learning , 2014, Artif. Intell..
[101] Seungjin Choi,et al. Bayesian common spatial patterns for multi-subject EEG classification , 2014, Neural Networks.
[102] Magda Tsolaki,et al. Functional disorganization of small-world brain networks in mild Alzheimer's Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE) , 2014, Front. Aging Neurosci..
[103] Zhong Yin,et al. Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques , 2014, Comput. Methods Programs Biomed..
[104] Diane J. Cook,et al. Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.
[105] Jie Cao,et al. A novel neural network approach to cDNA microarray image segmentation , 2013, Comput. Methods Programs Biomed..
[106] Wojciech Samek,et al. Transferring Subspaces Between Subjects in Brain--Computer Interfacing , 2012, IEEE Transactions on Biomedical Engineering.
[107] Anatole Lécuyer,et al. Exploring two novel features for EEG-based brain-computer interfaces: Multifractal cumulants and predictive complexity , 2010, Neurocomputing.
[108] Zidong Wang,et al. Cellular Neural Networks, the Navier–Stokes Equation, and Microarray Image Reconstruction , 2011, IEEE Transactions on Image Processing.
[109] Pei Wang,et al. Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning , 2010, Neural Computing and Applications.
[110] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[111] Esther Rodriguez-Villegas,et al. Wearable Electroencephalography , 2010, IEEE Engineering in Medicine and Biology Magazine.
[112] Leontios J. Hadjileontiadis,et al. Emotion Recognition From EEG Using Higher Order Crossings , 2010, IEEE Transactions on Information Technology in Biomedicine.
[113] Seungjin Choi,et al. Composite Common Spatial Pattern for Subject-to-Subject Transfer , 2009, IEEE Signal Processing Letters.
[114] Abdullah Al Mamun,et al. Weighted locally linear embedding for dimension reduction , 2009, Pattern Recognit..
[115] Cuntai Guan,et al. Unsupervised Brain Computer Interface Based on Intersubject Information and Online Adaptation , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[116] Hongbin Zha,et al. Riemannian Manifold Learning , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[117] K.-R. Muller,et al. Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.
[118] Klaus-Robert Müller,et al. The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.
[119] Rajat Raina,et al. Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.
[120] C.W. Anderson,et al. Geometric subspace methods and time-delay embedding for EEG artifact removal and classification , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[121] U. Rajendra Acharya,et al. Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..
[122] Liu Qing. A Survey: Subspace Analysis for Face Recognition , 2003 .
[123] G. Pfurtscheller,et al. Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.
[124] G. Pfurtscheller,et al. Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.
[125] Sebastian Thrun,et al. Learning to Learn: Introduction and Overview , 1998, Learning to Learn.
[126] R. Mcclelland. Interpersonal Processes and Brain Sciences — a New Anthropology , 1996, European Psychiatry.
[127] L. R. Novick. Analogical transfer, problem similarity, and expertise. , 1988, Journal of experimental psychology. Learning, memory, and cognition.
[128] J. Shaw. Correlation and coherence analysis of the EEG: a selective tutorial review. , 1984, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.