暂无分享,去创建一个
[1] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[2] P Berg,et al. Dipole modelling of eye activity and its application to the removal of eye artefacts from the EEG and MEG. , 1991, Clinical physics and physiological measurement : an official journal of the Hospital Physicists' Association, Deutsche Gesellschaft fur Medizinische Physik and the European Federation of Organisations for Medical Physics.
[3] T. Lagerlund,et al. Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. , 1997, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.
[4] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[5] G. Comi,et al. IFCN standards for digital recording of clinical EEG. International Federation of Clinical Neurophysiology. , 1998, Electroencephalography and clinical neurophysiology.
[6] J C Rothwell,et al. EMG-EEG correlation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.
[7] T. Sejnowski,et al. Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.
[8] Erkki Oja,et al. Independent component approach to the analysis of EEG and MEG recordings , 2000, IEEE Transactions on Biomedical Engineering.
[9] Christopher J. James,et al. Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis , 2003, IEEE Transactions on Biomedical Engineering.
[10] Arnaud Delorme,et al. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.
[11] Elena Urrestarazu,et al. Independent Component Analysis Removing Artifacts in Ictal Recordings , 2004, Epilepsia.
[12] C. Joyce,et al. Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. , 2004, Psychophysiology.
[13] B. W. Jervis,et al. Comparison of two methods for correcting ocular artefacts in EEGs , 1994, Medical and Biological Engineering and Computing.
[14] Terrence J. Sejnowski,et al. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis , 2007, NeuroImage.
[15] Miguel Angel Mañanas,et al. A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: A simulation case , 2008, Comput. Biol. Medicine.
[16] 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.
[17] A. Mognon,et al. ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.
[18] Robert Oostenveld,et al. FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..
[19] Gary E. Birch,et al. Online Removal of Eye Movement and Blink EEG Artifacts Using a High-Speed Eye Tracker , 2012, IEEE Transactions on Biomedical Engineering.
[20] Thierry Pun,et al. DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.
[21] Tomás Ward,et al. Artifact Removal in Physiological Signals—Practices and Possibilities , 2012, IEEE Transactions on Information Technology in Biomedicine.
[22] Heung-Il Suk,et al. A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] M. Lo,et al. Correlations between the Signal Complexity of Cerebral and Cardiac Electrical Activity: A Multiscale Entropy Analysis , 2014, PloS one.
[24] Yang Li,et al. Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications , 2014, IEEE Transactions on NanoBioscience.
[25] Stephanie Brandl,et al. Robust artifactual independent component classification for BCI practitioners , 2014, Journal of neural engineering.
[26] Begoña Garcia-Zapirain,et al. EEG artifact removal—state-of-the-art and guidelines , 2015, Journal of neural engineering.
[27] B. Meffert,et al. EEG artifact elimination by extraction of ICA-component features using image processing algorithms , 2015, Journal of Neuroscience Methods.
[28] Danilo Avola,et al. Basis for the implementation of an EEG-based single-trial binary brain computer interface through the disgust produced by remembering unpleasant odors , 2015, Neurocomputing.
[29] Andrea Petracca,et al. EEG-detected olfactory imagery to reveal covert consciousness in minimally conscious state , 2015, Brain injury.
[30] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[31] Tzyy-Ping Jung,et al. Real-Time Adaptive EEG Source Separation Using Online Recursive Independent Component Analysis , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[32] Amir Rastegarnia,et al. Methods for artifact detection and removal from scalp EEG: A review , 2016, Neurophysiologie Clinique/Clinical Neurophysiology.
[33] Thea Radüntz,et al. Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features , 2017, Journal of neural engineering.
[34] Francisco J. Pelayo,et al. Trends in EEG-BCI for daily-life: Requirements for artifact removal , 2017, Biomed. Signal Process. Control..
[35] Hai Tao,et al. Review of deep convolution neural network in image classification , 2017, 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET).
[36] Myung Yung Jeong,et al. Identification and Removal of Physiological Artifacts From Electroencephalogram Signals: A Review , 2018, IEEE Access.
[37] Masaki Shuzo,et al. Application of CNN for Human Activity Recognition with FFT Spectrogram of Acceleration and Gyro Sensors , 2018, UbiComp/ISWC Adjunct.
[38] Zhipeng Liu,et al. A method for recognition and classification for hybrid signals based on Deep Convolutional Neural Network , 2018, 2018 International Conference on Electronics Technology (ICET).
[39] Mingxuan Sun,et al. A Deep Multi-Modal CNN for Multi-Instance Multi-Label Image Classification , 2018, IEEE Transactions on Image Processing.
[40] Ganesh R. Naik,et al. Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG , 2018, Comput. Methods Programs Biomed..
[41] Mohammad Reza Mohammadi,et al. Modeling the connections of brain regions in children with autism using cellular neural networks and electroencephalography analysis , 2018, Artif. Intell. Medicine.
[42] Konda Reddy Mopuri,et al. CNN Fixations: An Unraveling Approach to Visualize the Discriminative Image Regions , 2019, IEEE Transactions on Image Processing.
[43] Kenneth Kreutz-Delgado,et al. ICLabel: An automated electroencephalographic independent component classifier, dataset, and website , 2019, NeuroImage.
[44] Sarang S. Dalal,et al. Reducing power line noise in EEG and MEG data via spectrum interpolation , 2019, NeuroImage.
[45] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[46] Xianlun Tang,et al. Motor imagery EEG recognition with KNN-based smooth auto-encoder , 2019, Artif. Intell. Medicine.
[47] Sepideh Hajipour Sardouie,et al. A novel hybrid BCI speller based on RSVP and SSVEP paradigm , 2020, Comput. Methods Programs Biomed..
[48] Varun Bajaj,et al. A facile and flexible motor imagery classification using electroencephalogram signals , 2020, Comput. Methods Programs Biomed..
[49] Tengyu Zhang,et al. Effects of fatigue on steady state motion visual evoked potentials: Optimised stimulus parameters for a zoom motion-based brain-computer interface , 2020, Comput. Methods Programs Biomed..
[50] Ping Gao,et al. Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks , 2020, Artif. Intell. Medicine.
[51] Yunhao Liu,et al. Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition , 2020, IEEE Transactions on Cybernetics.
[52] Terrence J. Sejnowski,et al. Utilizing Deep Learning Towards Multi-Modal Bio-Sensing and Vision-Based Affective Computing , 2019, IEEE Transactions on Affective Computing.