Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation
暂无分享,去创建一个
Kornkanok Tripanpitak | Waranrach Viriyavit | Shao Ying Huang | Wenwei Yu | Wenwei Yu | S. Huang | Kornkanok Tripanpitak | W. Viriyavit
[1] Doaa Shawky,et al. Characterizing Focused Attention and Working Memory Using EEG , 2018, Sensors.
[2] Kyungmin Su,et al. The PREP pipeline: standardized preprocessing for large-scale EEG analysis , 2015, Front. Neuroinform..
[3] A Mouraux,et al. Non-phase locked electroencephalogram (EEG) responses to CO2 laser skin stimulations may reflect central interactions between A∂- and C-fibre afferent volleys , 2003, Clinical Neurophysiology.
[4] Scott Makeig,et al. Modeling brain dynamic state changes with adaptive mixture independent component analysis , 2018, NeuroImage.
[5] Arnab Roy,et al. Automated classification of pain perception using high-density electroencephalography data. , 2017, Journal of neurophysiology.
[6] Aleksandra Vuckovic,et al. Prediction of central neuropathic pain in spinal cord injury based on EEG classifier , 2018, Clinical Neurophysiology.
[7] C. Warfield,et al. The measurement of pain. , 1988, Hospital practice.
[8] P. Agostino Accardo,et al. Use of the fractal dimension for the analysis of electroencephalographic time series , 1997, Biological Cybernetics.
[9] Klaus-Robert Müller,et al. On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[10] T. Higuchi. Approach to an irregular time series on the basis of the fractal theory , 1988 .
[11] 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.
[12] Martin Tegenthoff,et al. High test-retest-reliability of pain-related evoked potentials (PREP) in healthy subjects , 2017, Neuroscience Letters.
[13] Alexandros T. Tzallas,et al. Automated Assessment of Pain Intensity Based on EEG Signal Analysis , 2019, 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE).
[14] Mohammad Reza Khayyambashi,et al. Real-Time Traffic Classification Based on Statistical and Payload Content Features , 2010, 2010 2nd International Workshop on Intelligent Systems and Applications.
[15] Nikola Bogunovic,et al. A review of feature selection methods with applications , 2015, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).
[16] Kenneth Kreutz-Delgado,et al. AMICA : An Adaptive Mixture of Independent Component Analyzers with Shared Components , 2011 .
[17] Tzyy-Ping Jung,et al. Real-time neuroimaging and cognitive monitoring using wearable dry EEG , 2015, IEEE Transactions on Biomedical Engineering.
[18] Bin He,et al. Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models , 2017, IEEE Transactions on Biomedical Engineering.
[19] M F Schlesinger. Fractal time and 1/f noise in complex systems. , 1987, Annals of the New York Academy of Sciences.
[20] N. Crone,et al. Attention to a painful cutaneous laser stimulus modulates electrocorticographic event-related desynchronization in humans , 2004, Clinical Neurophysiology.
[21] Gene H. Golub,et al. Algorithms for Computing the Sample Variance: Analysis and Recommendations , 1983 .
[22] P. Grassberger,et al. Measuring the Strangeness of Strange Attractors , 1983 .
[23] Brian Litt,et al. A comparison of waveform fractal dimension algorithms , 2001 .
[24] R. Oostenveld,et al. Independent EEG Sources Are Dipolar , 2012, PloS one.
[25] Claudia Plant,et al. Decoding an individual's sensitivity to pain from the multivariate analysis of EEG data. , 2012, Cerebral cortex.
[26] Srdjan Kesic,et al. Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A review , 2016, Comput. Methods Programs Biomed..
[27] Sung Hoon Kim,et al. Pain-Related Evoked Potential in Healthy Adults , 2015, Annals of rehabilitation medicine.
[28] Mei Ying Boon,et al. The correlation dimension: a useful objective measure of the transient visual evoked potential? , 2008, Journal of vision.
[29] Joachim Gross,et al. Brain oscillations differentially encode noxious stimulus intensity and pain intensity , 2017, NeuroImage.
[30] Alon Sinai,et al. Tonic pain and continuous EEG: Prediction of subjective pain perception by alpha-1 power during stimulation and at rest , 2012, Clinical Neurophysiology.
[31] C. Graversen,et al. Dynamic spectral indices of the electroencephalogram provide new insights into tonic pain , 2015, Clinical Neurophysiology.
[32] Zhaohui Yuan,et al. Motor Imagery EEG Signals Classification Based on Mode Amplitude and Frequency Components Using Empirical Wavelet Transform , 2019, IEEE Access.
[33] Yanchun Zhang,et al. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating , 2016, Comput. Methods Programs Biomed..
[34] Jiawei Han,et al. Generalized Fisher Score for Feature Selection , 2011, UAI.
[35] Jianda Han,et al. Physiological Signal-Based Method for Measurement of Pain Intensity , 2017, Front. Neurosci..
[36] Lianqing Zhu,et al. Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG , 2020, Neurocomputing.
[37] Koji Inui,et al. Selective Stimulation of C Fibers by an Intra-Epidermal Needle Electrode in Humans , 2009 .
[38] Tzyy-Ping Jung,et al. Evaluation of Artifact Subspace Reconstruction for Automatic EEG Artifact Removal , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[39] J. Petrofsky,et al. The transfer of current through skin and muscle during electrical stimulation with sine, square, Russian and interferential waveforms , 2009, Journal of medical engineering & technology.
[40] Reza Rostami,et al. Classifying depression patients and normal subjects using machine learning techniques , 2011, 2011 19th Iranian Conference on Electrical Engineering.
[41] D. Abásolo,et al. Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients. , 2009, Medical Engineering and Physics.
[42] S L Notermans,et al. Measurement of the pain threshold determined by electrical stimulation and its clinical application , 1966, Neurology.
[43] Alexandros T. Tzallas,et al. Analysis of electroencephalographic signals complexity regarding Alzheimer's Disease , 2019, Comput. Electr. Eng..
[44] P. Grassberger,et al. Characterization of Strange Attractors , 1983 .
[45] C. Stam,et al. Decrease of non-linear structure in the EEG of Alzheimer patients compared to healthy controls , 1999, Clinical Neurophysiology.
[46] Alon Sinai,et al. Pain assessment by continuous EEG: Association between subjective perception of tonic pain and peak frequency of alpha oscillations during stimulation and at rest , 2010, Brain Research.
[47] Satoshi Kuwabara,et al. Pain-related evoked potentials after intraepidermal electrical stimulation to Aδ and C fibers in patients with neuropathic pain , 2017, Neuroscience Research.
[48] Cheng-Lin Liu,et al. Feature Selection by Combining Fisher Criterion and Principal Feature Analysis , 2007, 2007 International Conference on Machine Learning and Cybernetics.
[49] Christian Flores Vega,et al. Parameters analyzed of Higuchi's fractal dimension for EEG brain signals , 2015, 2015 Signal Processing Symposium (SPSympo).
[50] G. Lancaster,et al. Validation of the Alder Hey Triage Pain Score , 2004, Archives of Disease in Childhood.
[51] F. Mauguière,et al. Scalp topography and dipolar source modelling of potentials evoked by CO2 laser stimulation of the hand. , 1996, Electroencephalography and clinical neurophysiology.
[52] Hiie Hinrikus,et al. Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis , 2018, Comput. Methods Programs Biomed..