Quantifying Different Tactile Sensations Evoked by Cutaneous Electrical Stimulation Using Electroencephalography Features

Psychophysical tests and standardized questionnaires are often used to analyze tactile sensation based on subjective judgment in conventional studies. In contrast with the subjective evaluation, a novel method based on electroencephalography (EEG) is proposed to explore the possibility of quantifying tactile sensation in an objective way. The proposed experiments adopt cutaneous electrical stimulation to generate two kinds of sensations (vibration and pressure) with three grades (low/medium/strong) on eight subjects. Event-related potentials (ERPs) and event-related synchronization/desynchronization (ERS/ERD) are extracted from EEG, which are used as evaluation indexes to distinguish between vibration and pressure, and also to discriminate sensation grades. Results show that five-phase P1–N1–P2–N2–P3 deflection is induced in EEG. Using amplitudes of latter ERP components (N2 and P3), vibration and pressure sensations can be discriminated on both individual and grand-averaged ERP (p < 0.05). The grand-average ERPs can distinguish the three sensations grades, but there is no significant difference on individuals. In addition, ERS/ERD features of mu rhythm (8–13 Hz) are adopted. Vibration and pressure sensations can be discriminated on grand-average ERS/ERD (p < 0.05), but only some individuals show significant difference. The grand-averaged results show that most sensation grades can be differentiated, and most pairwise comparisons show significant difference on individuals (p < 0.05). The work suggests that ERP- and ERS/ERD-based EEG features may have potential to quantify tactile sensations for medical diagnosis or engineering applications.

[1]  A. Revonsuo,et al.  Does P3 reflect attentional or memory performances, or cognition more generally? , 2000, Scandinavian journal of psychology.

[2]  Tianwei Shi,et al.  Real-Time EEG-Based Detection of Fatigue Driving Danger for Accident Prediction , 2015, Int. J. Neural Syst..

[3]  P. Davies,et al.  Validating the diagnosis of sensory processing disorders using EEG technology. , 2007, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[4]  Jianjun Meng,et al.  Simultaneously Optimizing Spatial Spectral Features Based on Mutual Information for EEG Classification , 2015, IEEE Transactions on Biomedical Engineering.

[5]  Hong Wang,et al.  EEG-based expert system using complexity measures and probability density function control in alpha sub-band , 2013, Integr. Comput. Aided Eng..

[6]  S S Hsiao,et al.  Detection of vibration transmitted through an object grasped in the hand. , 1999, Journal of neurophysiology.

[7]  Sebastian Merchel,et al.  Electrotactile Feedback for Handheld Devices with Touch Screen and Simulation of Roughness , 2012, IEEE Transactions on Haptics.

[8]  H. Adeli,et al.  Brain-computer interface technologies: from signal to action , 2013, Reviews in the neurosciences.

[9]  F. Rattay Analysis of Models for External Stimulation of Axons , 1986, IEEE Transactions on Biomedical Engineering.

[10]  Akio Yamamoto,et al.  Electrostatic tactile display with thin film slider and its application to tactile telepresentation systems , 2004, IEEE Transactions on Visualization and Computer Graphics.

[11]  G. Pfurtscheller,et al.  Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients , 2007, Brain Research.

[12]  Gian Domenico Iannetti,et al.  A novel approach to predict subjective pain perception from single-trial laser-evoked potentials , 2013, NeuroImage.

[13]  Joseph P. McCleery,et al.  EEG evidence for mirror neuron dysfunction in autism spectrum disorders. , 2005, Brain research. Cognitive brain research.

[14]  Andrew C. N. Chen,et al.  Exploration of somatosensory P50 gating in schizophrenia spectrum patients: reduced P50 amplitude correlates to social anhedonia , 2004, Psychiatry Research.

[15]  J. Roll,et al.  Response to pressure and vibration of slowly adapting cutaneous mechanoreceptors in the human foot , 1982, Neuroscience Letters.

[16]  Wei-Yen Hsu,et al.  Assembling A Multi-Feature EEG Classifier for Left-Right Motor Imagery Data Using Wavelet-Based Fuzzy Approximate Entropy for Improved Accuracy , 2015, Int. J. Neural Syst..

[17]  Weidong Zhou,et al.  Epileptic EEG Classification Based on Kernel Sparse Representation , 2014, Int. J. Neural Syst..

[18]  Marco Santello,et al.  Receptive Field Characteristics Under Electrotactile Stimulation of the Fingertip , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Xiangyang Zhu,et al.  Somatotopical feedback versus non-somatotopical feedback for phantom digit sensation on amputees using electrotactile stimulation , 2015, Journal of NeuroEngineering and Rehabilitation.

[20]  J. Buford,et al.  Wavelet methodology to improve single unit isolation in primary motor cortex cells , 2015, Journal of Neuroscience Methods.

[21]  P. Derambure,et al.  Relationship between event-related beta synchronization and afferent inputs: Analysis of finger movement and peripheral nerve stimulations , 2006, Clinical Neurophysiology.

[22]  William P. Marnane,et al.  Robust neonatal EEG seizure Detection through Adaptive Background Modeling , 2013, Int. J. Neural Syst..

[23]  G.S. Dhillon,et al.  Direct neural sensory feedback and control of a prosthetic arm , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  B. Kopp,et al.  N2, P3 and the lateralized readiness potential in a nogo task involving selective response priming. , 1996, Electroencephalography and clinical neurophysiology.

[25]  J. Buford,et al.  Brain–Computer Interface after Nervous System Injury , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[26]  M. Diamond,et al.  Tactile perception and working memory in rats and humans , 2014, Proceedings of the National Academy of Sciences.

[27]  G. M. Bairy,et al.  Automated diagnosis of autism: in search of a mathematical marker , 2014, Reviews in the neurosciences.

[28]  C. C. Wood,et al.  The relationship between human long-latency somatosensory evoked potentials recorded from the cortical surface and from the scalp. , 1992, Electroencephalography and clinical neurophysiology.

[29]  Dennis J. McFarland,et al.  The P300-based brain–computer interface (BCI): Effects of stimulus rate , 2011, Clinical Neurophysiology.

[30]  Minho Lee,et al.  Action-perception cycle learning for incremental emotion recognition in a movie clip using 3D fuzzy GIST based on visual and EEG signals , 2014, Integr. Comput. Aided Eng..

[31]  Martin Eimer,et al.  Crossmodal links in spatial attention between vision, audition, and touch: evidence from event-related brain potentials , 2001, Neuropsychologia.

[32]  Salil H. Patel,et al.  Impact of instructed relevance on the visual ERP. , 2004, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[33]  Qiaorong Su,et al.  Development and psychometric properties of an informant assessment scale of theory of mind for adults with traumatic brain injury , 2016, Neuropsychological rehabilitation.

[34]  Weidong Zhou,et al.  Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG , 2015, Int. J. Neural Syst..

[35]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[36]  A. Mouraux,et al.  Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression. , 2011, Journal of neurophysiology.

[37]  W.J. Tompkins,et al.  Electrotactile and vibrotactile displays for sensory substitution systems , 1991, IEEE Transactions on Biomedical Engineering.

[38]  Pedro J. García-Laencina,et al.  Efficient Automatic Selection and Combination of EEG Features in Least Squares Classifiers for Motor Imagery Brain-Computer Interfaces , 2013, Int. J. Neural Syst..

[39]  Gert Pfurtscheller,et al.  Alpha power dependent light stimulation: dynamics of event-related (de)synchronization in human electroencephalogram. , 2004, Brain research. Cognitive brain research.

[40]  Christopher J. Poletto,et al.  Elevating pain thresholds in humans using depolarizing prepulses , 2002, IEEE Transactions on Biomedical Engineering.

[41]  R. Romo,et al.  Decoding stimulus features in primate somatosensory cortex during perceptual categorization , 2015, Proceedings of the National Academy of Sciences.

[42]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[43]  A. Mouraux,et al.  Gamma-Band Oscillations in the Primary Somatosensory Cortex—A Direct and Obligatory Correlate of Subjective Pain Intensity , 2012, The Journal of Neuroscience.

[44]  Kenneth O. Johnson,et al.  The roles and functions of cutaneous mechanoreceptors , 2001, Current Opinion in Neurobiology.

[45]  G Pfurtscheller,et al.  Discrimination between phase-locked and non-phase-locked event-related EEG activity. , 1995, Electroencephalography and clinical neurophysiology.

[46]  Robert Freedman,et al.  The P50 auditory event–evoked potential in adult attention-deficit disorder: comparison with schizophrenia , 2000, Biological Psychiatry.

[47]  Andrew C. N. Chen,et al.  Laser-evoked potentials in human pain: I. Use and possible misuse , 1998 .

[48]  Susan S Johnston,et al.  Sensory processing disorders and social participation. , 2010, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[49]  A. Stancák,et al.  Desynchronization of cortical rhythms following cutaneous stimulation: effects of stimulus repetition and intensity, and of the size of corpus callosum , 2003, Clinical Neurophysiology.

[50]  J. Wolpaw,et al.  Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements , 2004, Brain Topography.

[51]  Giuseppina C. Gini,et al.  A Multi-Modal Haptic Interface for Virtual Reality and Robotics , 2008, J. Intell. Robotic Syst..

[52]  K.A. Kaczmarek,et al.  Pattern identification as a function of stimulation on a fingertip-scanned electrotactile display , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[53]  M. Diamond,et al.  Neuronal Activity in Rat Barrel Cortex Underlying Texture Discrimination , 2007, PLoS biology.

[54]  F. Rattay Analysis of models for extracellular fiber stimulation , 1989, IEEE Transactions on Biomedical Engineering.

[55]  Saeid Sanei,et al.  A Predictive Modeling Approach to Analyze Data in EEG-fMRI Experiments , 2015, Int. J. Neural Syst..

[56]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[57]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[58]  Kwee-Bo Sim,et al.  ERS and ERD analysis during the imaginary movement of arms , 2008, 2008 International Conference on Control, Automation and Systems.

[59]  K Satomi,et al.  Hemispheric asymmetry of event-related potentials in a patient with callosal disconnection syndrome: a comparison of auditory, visual and somatosensory modalities. , 1995, Electroencephalography and clinical neurophysiology.

[60]  K. Kaczmarek,et al.  Electrotactile adaptation on the abdomen: preliminary results. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[61]  Saskia Haegens,et al.  Thalamocortical rhythms during a vibrotactile detection task , 2014, Proceedings of the National Academy of Sciences.

[62]  G Pfurtscheller,et al.  Contrasting behavior of beta event-related synchronization and somatosensory evoked potential after median nerve stimulation during finger manipulation in man , 2002, Neuroscience Letters.

[63]  O. Witte,et al.  Sensory feedback prosthesis reduces phantom limb pain: Proof of a principle , 2012, Neuroscience Letters.

[64]  M E Tyler,et al.  The afferent neural response to electrotactile stimuli: preliminary results. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[65]  D. G. Buma,et al.  Intermittent Stimulation Delays Adaptation to Electrocutaneous Sensory Feedback , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.