Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived...

[1]  Andrés Marino Álvarez-Meza,et al.  Time-series discrimination using feature relevance analysis in motor imagery classification , 2015, Neurocomputing.

[2]  Maysam F. Abbod,et al.  EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks , 2015, Comput. Math. Methods Medicine.

[3]  Maja Cic,et al.  Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal , 2013, Comput. Biol. Medicine.

[4]  Yang Jinfu,et al.  A novel EEG feature extraction method based on OEMD and CSP algorithm , 2016 .

[5]  Dheeraj Sharma,et al.  Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications , 2018 .

[6]  Maysam F. Abbod,et al.  EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery , 2016, Medical & Biological Engineering & Computing.

[7]  Slawomir J. Nasuto,et al.  The application of the Hilbert spectrum to the analysis of electromyographic signals , 2008, Inf. Sci..

[8]  Nigel H. Lovell,et al.  Erratum to “Optimisation of a Generic Ionic Model of Cardiac Myocyte Electrical Activity” , 2013, Comput. Math. Methods Medicine.

[9]  Gang Wang,et al.  The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition , 2016, IEEE Journal of Biomedical and Health Informatics.

[10]  G. Curio,et al.  Task‐related differential dynamics of EEG alpha‐ and beta‐band synchronization in cortico‐basal motor structures , 2007, The European journal of neuroscience.

[11]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[12]  Chi Zhang,et al.  Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain , 2017, BioMed research international.

[13]  G Carrault,et al.  Denoising preterm EEG by signal decomposition and adaptive filtering: a comparative study. , 2015, Medical engineering & physics.

[14]  Slawomir J. Nasuto,et al.  Neural Correlates of True and False Memory in Mild Cognitive Impairment , 2012, PloS one.

[15]  M. Farge.  Wavelet Transforms and their Applications to Turbulence , 1992 .

[16]  You Wang,et al.  Effect of intermittent hypoxic training on hypoxia tolerance based on single-channel EEG , 2016, Neuroscience Letters.

[17]  Amir Rastegarnia,et al.  Artifact characterization and removal for in vivo neural recording , 2014, Journal of Neuroscience Methods.

[18]  Po-Lei Lee,et al.  Extraction of single-trial cortical beta oscillatory activities in EEG signals using empirical mode decomposition , 2010, Biomedical engineering online.

[19]  F. Varela,et al.  Guiding the study of brain dynamics by using first-person data: Synchrony patterns correlate with ongoing conscious states during a simple visual task , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[21]  Men-Tzung Lo,et al.  Empirical mode decomposition based detrended sample entropy in electroencephalography for Alzheimer's disease , 2012, Journal of Neuroscience Methods.

[22]  Abhishek Vaish,et al.  Information-Theoretic Measures on Intrinsic Mode Function for the Individual Identification Using EEG Sensors , 2015, IEEE Sensors Journal.

[23]  Aamir Saeed Malik,et al.  Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA , 2017, Journal of Neuroscience Methods.

[24]  Kup-Sze Choi,et al.  Discrimination of motor imagery tasks via information flow pattern of brain connectivity. , 2016, Technology and health care : official journal of the European Society for Engineering and Medicine.

[25]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[26]  V. A. Makarov,et al.  Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis , 2006, Journal of Neuroscience Methods.

[27]  S. Bressler,et al.  Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Yuesheng Xu,et al.  Recent Mathematical Developments on Empirical Mode Decomposition , 2009, Adv. Data Sci. Adapt. Anal..

[29]  G. Pfurtscheller,et al.  Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[30]  Tomasz M. Rutkowski,et al.  Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users , 2014, Journal of Neuroscience Methods.

[31]  Jasmin Kevric,et al.  Biomedical Signal Processing and Control , 2016 .

[32]  Banghua Yang,et al.  Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface , 2015, Frontiers of Information Technology & Electronic Engineering.

[33]  Boreom Lee,et al.  EEG classification in a single-trial basis for vowel speech perception using multivariate empirical mode decomposition , 2014, Journal of neural engineering.

[34]  Ram Bilas Pachori,et al.  Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition , 2011, Comput. Methods Programs Biomed..

[35]  Tao Zhang,et al.  Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition , 2017, Cognitive Computation.

[36]  Jing Zhao,et al.  ENERGY FEATURE EXTRACTION AND SVM CLASSIFICATION OF MOTOR IMAGERY-INDUCED ELECTROENCEPHALOGRAMS , 2012 .

[37]  Suman Dutta,et al.  Classification of non-motor cognitive task in EEG based brain-computer interface using phase space features in multivariate empirical mode decomposition domain , 2018, Biomed. Signal Process. Control..

[38]  Mohammed Imamul Hassan Bhuiyan,et al.  Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting , 2017, Comput. Methods Programs Biomed..

[39]  Alexandre Andrade,et al.  Causal inference in neuronal time-series using adaptive decomposition , 2015, Journal of Neuroscience Methods.

[40]  Genshe Chen,et al.  Progress in EEG-Based Brain Robot Interaction Systems , 2017, Comput. Intell. Neurosci..

[41]  U. Rajendra Acharya,et al.  Application of Empirical Mode Decomposition (EMD) for Automated Detection of epilepsy using EEG signals , 2012, Int. J. Neural Syst..

[42]  Alexey N. Pavlov,et al.  Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets , 2017 .

[43]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[44]  Bradley Voytek,et al.  Nonsinusoidal Beta Oscillations Reflect Cortical Pathophysiology in Parkinson's Disease , 2017, The Journal of Neuroscience.

[45]  Leontios J. Hadjileontiadis,et al.  Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis , 2010, IEEE Transactions on Affective Computing.

[46]  Luciano Boquete,et al.  Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition , 2017, Sensors.

[47]  Po-Lei Lee,et al.  Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined generalized zero-crossing , 2011, Journal of Neuroscience Methods.

[48]  Yi-Chun Chen,et al.  Comparison of FFT and marginal spectra of EEG using empirical mode decomposition to monitor anesthesia , 2016, Comput. Methods Programs Biomed..

[49]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[50]  Slawomir J. Nasuto,et al.  On the Phase Coupling of Two Components Mixing in Empirical Mode Decomposition , 2016, Adv. Data Sci. Adapt. Anal..

[51]  M. Wibral,et al.  Untangling cross-frequency coupling in neuroscience , 2014, Current Opinion in Neurobiology.

[52]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[53]  Tjeerd W. Boonstra,et al.  Upregulation of cortico-cerebellar functional connectivity after motor learning , 2016, NeuroImage.

[54]  Rifai Chai,et al.  Brain–Computer Interface Classifier for Wheelchair Commands Using Neural Network With Fuzzy Particle Swarm Optimization , 2014, IEEE Journal of Biomedical and Health Informatics.

[55]  R. D. Pascual-Marqui,et al.  The EEG microstate topography is predominantly determined by intracortical sources in the alpha band , 2017, NeuroImage.

[56]  Tao Qian,et al.  Mono-Components vs Imfs in Signal Decomposition , 2008, Int. J. Wavelets Multiresolution Inf. Process..

[57]  Wei-Ta Chen,et al.  Increased Intrinsic Connectivity of the Default Mode Network in Temporal Lobe Epilepsy: Evidence from Resting-State MEG Recordings , 2015, PloS one.

[58]  Aydin Akan,et al.  Emotion recognition from EEG signals by using multivariate empirical mode decomposition , 2018, Pattern Analysis and Applications.

[59]  Xueyuan Xu,et al.  The Use of Multivariate EMD and CCA for Denoising Muscle Artifacts From Few-Channel EEG Recordings , 2018, IEEE Transactions on Instrumentation and Measurement.

[60]  E. Spaak,et al.  Discriminating Valid from Spurious Indices of Phase-Amplitude Coupling , 2016, eNeuro.

[61]  Zuren Feng,et al.  Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification , 2015, Comput. Biol. Medicine.

[62]  Toshihisa Tanaka,et al.  Interactive component extraction from fEEG, fNIRS and peripheral biosignals for affective brain-machine interfacing paradigms , 2011, Comput. Hum. Behav..

[63]  Enzo Tagliazucchi,et al.  Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities , 2017, NeuroImage.

[64]  Hualou Liang,et al.  Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition, Clustering, and Support Vector Machine , 2008, EURASIP J. Adv. Signal Process..

[65]  Seán F. McLoone,et al.  The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique , 2013, IEEE Transactions on Biomedical Engineering.

[66]  M G Jibukumar,et al.  Ictal EEG classification based on amplitude and frequency contours of IMFs , 2017 .

[67]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[68]  Alexandre Andrade,et al.  Instantaneous Granger Causality with the Hilbert-Huang Transform , 2013 .

[69]  Joydeep Bhattacharya,et al.  Correction of blink artifacts using independent component analysis and empirical mode decomposition. , 2010, Psychophysiology.

[70]  Ian Daly,et al.  Brain computer interface control via functional connectivity dynamics , 2012, Pattern Recognit..

[71]  Rami J Oweis,et al.  Seizure classification in EEG signals utilizing Hilbert-Huang transform , 2011, Biomedical engineering online.

[72]  Rainer Hammwöhner,et al.  Ensemble Empirical Mode Decomposition Analysis of EEG Data Collected during a Contour Integration Task , 2015, PloS one.

[73]  Mirka Pesonen,et al.  Brain oscillatory 4–30 Hz responses during a visual n-back memory task with varying memory load , 2007, Brain Research.

[74]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[75]  Cheolsoo Park,et al.  Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[76]  Lizhe Wang,et al.  An EEMD-ICA Approach to Enhancing Artifact Rejection for Noisy Multivariate Neural Data , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[77]  Manoranjan Paul,et al.  Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals Using Phase Correlation , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[78]  S. Debener,et al.  Default-mode brain dysfunction in mental disorders: A systematic review , 2009, Neuroscience & Biobehavioral Reviews.

[79]  Zhijie Bian,et al.  Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus , 2015 .

[80]  Men-Tzung Lo,et al.  Spurious cross-frequency amplitude-amplitude coupling in nonstationary, nonlinear signals. , 2016, Physica A.

[81]  R. Sharpley,et al.  Analysis of the Intrinsic Mode Functions , 2006 .

[82]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[83]  Slawomir J. Nasuto,et al.  A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition , 2007, Journal of Computational Neuroscience.

[84]  Young-Hui Chang,et al.  Autogenic EMG-controlled functional electrical stimulation for ankle dorsiflexion control , 2010, Journal of Neuroscience Methods.

[85]  Hans-Jochen Heinze,et al.  Corticothalamic phase synchrony and cross-frequency coupling predict human memory formation , 2014, eLife.

[86]  K. Zaghloul,et al.  Dual origins of measured phase-amplitude coupling reveal distinct neural mechanisms underlying episodic memory in the human cortex , 2016, NeuroImage.

[87]  K. Al-Subari,et al.  EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition , 2015, Journal of Neuroscience Methods.

[88]  D. Looney,et al.  Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[89]  Vicente Alarcón Aquino,et al.  Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition , 2013, Sensors.

[90]  Amir Rastegarnia,et al.  Methods for artifact detection and removal from scalp EEG: A review , 2016, Neurophysiologie Clinique/Clinical Neurophysiology.

[91]  Qingshan She,et al.  Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification , 2016, Neural plasticity.

[92]  Liu Chuanwei,et al.  Discrimination of motor imagery patterns by electroencephalogram phase synchronization combined with frequency band energy , 2017 .

[93]  J. Martinerie,et al.  The brainweb: Phase synchronization and large-scale integration , 2001, Nature Reviews Neuroscience.

[94]  Anirban Dutta,et al.  Computational Pipeline for NIRS-EEG Joint Imaging of tDCS-Evoked Cerebral Responses—An Application in Ischemic Stroke , 2016, Front. Neurosci..

[95]  Peter J. Kyberd,et al.  EMG signal filtering based on Empirical Mode Decomposition , 2006, Biomed. Signal Process. Control..

[96]  R. Knight,et al.  The functional role of cross-frequency coupling , 2010, Trends in Cognitive Sciences.

[97]  Qingshan She,et al.  Noise-assisted MEMD based relevant IMFs identification and EEG classification , 2017, Journal of Central South University.

[98]  José Luis Pons Rovira,et al.  Empirical mode decomposition: a novel technique for the study of tremor time series , 2006, Medical and Biological Engineering and Computing.

[99]  Francisco J. Pelayo,et al.  Trends in EEG-BCI for daily-life: Requirements for artifact removal , 2017, Biomed. Signal Process. Control..

[100]  Klaus D. McDonald-Maier,et al.  Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition , 2017, Comput. Biol. Medicine.

[101]  Kim Dremstrup,et al.  EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[102]  M. Hallett,et al.  Functional coupling of human right and left cortical motor areas demonstrated with partial coherence analysis , 2000, Neuroscience Letters.

[103]  R. K. Agrawal,et al.  Performance enhancement of mental task classification using EEG signal: a study of multivariate feature selection methods , 2015, Soft Comput..

[104]  S. S. Shen,et al.  Applications of Hilbert–Huang transform to non‐stationary financial time series analysis , 2003 .

[105]  Shufang Li,et al.  Feature extraction and recognition of ictal EEG using EMD and SVM , 2013, Comput. Biol. Medicine.

[106]  E. Tangalos,et al.  Mild Cognitive Impairment Clinical Characterization and Outcome , 1999 .

[107]  Hongzhi Qi,et al.  A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action , 2010, Journal of neural engineering.

[108]  Jürgen Kurths,et al.  Detection of n:m Phase Locking from Noisy Data: Application to Magnetoencephalography , 1998 .

[109]  Hans-Jochen Heinze,et al.  Anterior Thalamic High Frequency Band Activity Is Coupled with Theta Oscillations at Rest , 2017, Front. Hum. Neurosci..

[110]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[111]  M. Corbetta,et al.  Electrophysiological signatures of resting state networks in the human brain , 2007, Proceedings of the National Academy of Sciences.

[112]  P. Uhlhaas,et al.  Working memory and neural oscillations: alpha–gamma versus theta–gamma codes for distinct WM information? , 2014, Trends in Cognitive Sciences.

[113]  A. E. Schulman,et al.  Functional coupling and regional activation of human cortical motor areas during simple, internally paced and externally paced finger movements. , 1998, Brain : a journal of neurology.

[114]  Pere Caminal,et al.  Prediction of Nociceptive Responses during Sedation by Linear and Non-Linear Measures of EEG Signals in High Frequencies , 2015, PloS one.

[115]  J. Fell,et al.  Cross-frequency coupling supports multi-item working memory in the human hippocampus , 2010, Proceedings of the National Academy of Sciences.

[116]  Wei-Kuang Liang,et al.  An improved method for measuring mismatch negativity using ensemble empirical mode decomposition , 2016, Journal of Neuroscience Methods.

[117]  Jongin Kim,et al.  EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[118]  Chia-Wei Sun,et al.  A Brain-Wave-Actuated Small Robot Car Using Ensemble Empirical Mode Decomposition-Based Approach , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[119]  Sabine Van Huffel,et al.  Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis , 2010, IEEE Transactions on Biomedical Engineering.

[120]  Claudio A. Perez,et al.  Automated Sleep-Spindle Detection in Healthy Children Polysomnograms , 2010, IEEE Transactions on Biomedical Engineering.

[121]  Vahid Abootalebi,et al.  Selection of an efficient feature space for EEG-based mental task discrimination , 2014 .

[122]  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.

[123]  Ram Bilas Pachori,et al.  Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition , 2012, IEEE Transactions on Information Technology in Biomedicine.

[124]  João Ricardo Sato,et al.  Application of Partial Directed Coherence to the Analysis of Resting-State EEG-fMRI Data , 2013, Brain Connect..

[125]  Aiguo Song,et al.  EOG Artifact Correction from EEG Recording Using Stationary Subspace Analysis and Empirical Mode Decomposition , 2013, Sensors.

[126]  Mircea Ariel Schoenfeld,et al.  A multivariate, spatiotemporal analysis of electromagnetic time-frequency data of recognition memory , 2003, NeuroImage.

[127]  Men-Tzung Lo,et al.  Inter-Trial Analysis of Post-Movement Beta Activities in EEG Signals Using Multivariate Empirical Mode Decomposition , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[128]  Danilo P. Mandic,et al.  Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[129]  Minyou Chen,et al.  Use of phase-locking value in sensorimotor rhythm-based brain–computer interface: zero-phase coupling and effects of spatial filters , 2017, Medical & Biological Engineering & Computing.

[130]  Hans-Jochen Heinze,et al.  Thalamic theta phase alignment predicts human memory formation and anterior thalamic cross-frequency coupling , 2015, eLife.

[131]  S. Cole,et al.  Brain Oscillations and the Importance of Waveform Shape , 2017, Trends in Cognitive Sciences.

[132]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[133]  Quan Liu,et al.  A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface , 2017, Journal of neural engineering.

[134]  E W Lang,et al.  A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation , 2017, Journal of neural engineering.

[135]  Cuntai Guan,et al.  Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis , 2017, IEEE Transactions on Biomedical Engineering.

[136]  Koushik Maharatna,et al.  Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG , 2016, Journal of Neuroscience Methods.

[137]  Francesco Carlo Morabito,et al.  On the Use of Empirical Mode Decomposition (EMD) for Alzheimer's Disease Diagnosis , 2015, Advances in Neural Networks.

[138]  C. Braun,et al.  Plasticity of premotor cortico-muscular coherence in severely impaired stroke patients with hand paralysis , 2017, NeuroImage: Clinical.

[139]  Mohammed Imamul Hassan Bhuiyan,et al.  Classification of motor imagery movements using multivariate empirical mode decomposition and short time Fourier transform based hybrid method , 2016 .

[140]  Nitish V. Thakor,et al.  The anterior contralateral response improves performance in a single trial auditory oddball BMI , 2015, Biomed. Signal Process. Control..

[141]  M. Berger,et al.  High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex , 2006, Science.

[142]  Benjamin A. Seitzman,et al.  Cognitive manipulation of brain electric microstates , 2017, NeuroImage.

[143]  Helmut Laufs,et al.  A stochastic model for EEG microstate sequence analysis , 2015, NeuroImage.

[144]  Catherine Marie Sweeney-Reed,et al.  Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis , 2009, Journal of Neuroscience Methods.