Characterization of Artifacts Produced by Gel Displacement on Non-invasive Brain-Machine Interfaces during Ambulation

So far, Brain-Machine Interfaces (BMIs) have been mainly used to study brain potentials during movement-free conditions. Recently, due to the emerging concern of improving rehabilitation therapies, these systems are also being used during gait experiments. Under this new condition, the evaluation of motion artifacts has become a critical point to assure the validity of the results obtained. Due to the high signal to noise ratio provided, the use of wet electrodes is a widely accepted technic to acquire electroencephalographic (EEG signals). To perform these recordings it is necessary to apply a conductive gel between the scalp and the electrodes. This work is focused on the study of gel displacements produced during ambulation and how they affect the amplitude of EEG signals. Data recorded during three ambulation conditions (gait training) and one movement-free condition (BMI motor imagery task) are compared to perform this study. Two phenomenons, manifested as unusual increases of the signals' amplitude, have been identified and characterized during this work. Results suggest that they are caused by abrupt changes on the conductivity between the electrode and the scalp due to gel displacement produced during ambulation and head movements. These artifacts significantly increase the Power Spectral Density (PSD) of EEG recordings at all frequencies from 5 to 90 Hz, corresponding to the main bandwidth of electrocortical potentials. They should be taken into consideration before performing EEG recordings in order to asses the correct gel allocation and to avoid the use of electrodes on certain scalp areas depending on the experimental conditions.

[1]  Christa Neuper,et al.  Rehabilitation with Brain-Computer Interface Systems , 2008, Computer.

[2]  Daniel P. Ferris,et al.  Removal of movement artifact from high-density EEG recorded during walking and running. , 2010, Journal of neurophysiology.

[3]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[4]  Gytis Baranauskas,et al.  What limits the performance of current invasive brain machine interfaces? , 2014, Front. Syst. Neurosci..

[5]  G. Cheron,et al.  About the cortical origin of the low-delta and high-gamma rhythms observed in EEG signals during treadmill walking , 2014, Neuroscience Letters.

[6]  Bryan Kolb,et al.  Searching for the principles of brain plasticity and behavior , 2014, Cortex.

[7]  W. Freeman,et al.  Spatial spectra of scalp EEG and EMG from awake humans , 2003, Clinical Neurophysiology.

[8]  M. Diamond,et al.  Experience-dependent plasticity of rat barrel cortex: redistribution of activity across barrel-columns. , 2000, Cerebral cortex.

[9]  Miguel A. L. Nicolelis,et al.  Principles of neural ensemble physiology underlying the operation of brain–machine interfaces , 2009, Nature Reviews Neuroscience.

[10]  Miguel A. L. Nicolelis,et al.  Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change , 2012, Front. Neuroeng..

[11]  J. Kalaska,et al.  Neural mechanisms for interacting with a world full of action choices. , 2010, Annual review of neuroscience.

[12]  R. Ward,et al.  EMG and EOG artifacts in brain computer interface systems: A survey , 2007, Clinical Neurophysiology.

[13]  An H. Do,et al.  Operation of a brain-computer interface walking simulator for individuals with spinal cord injury , 2013, Journal of NeuroEngineering and Rehabilitation.

[14]  Daniel P Ferris,et al.  Isolating gait-related movement artifacts in electroencephalography during human walking , 2015, Journal of neural engineering.

[15]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[16]  G. Logan.  On the ability to inhibit thought and action , 1984 .

[17]  M. Teplan.  FUNDAMENTALS OF EEG MEASUREMENT , 2002 .

[18]  R. Quiroga,et al.  Stationarity of the EEG series , 1995 .

[19]  Gong Chen,et al.  A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. , 2013, Critical reviews in biomedical engineering.

[20]  Daniel P. Ferris,et al.  Electrocortical activity is coupled to gait cycle phase during treadmill walking , 2011, NeuroImage.

[21]  Thierry Dutoit,et al.  A subjective assessment of a P300 BCI system for lower-limb rehabilitation purposes , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  J. R. Rosenberg,et al.  Coherent cortical and muscle discharge in cortical myoclonus. , 1999, Brain : a journal of neurology.

[23]  J. M. Azorín,et al.  Analyzing EEG signals to detect unexpected obstacles during walking , 2015, Journal of NeuroEngineering and Rehabilitation.

[24]  J. Wolpaw,et al.  EMG contamination of EEG: spectral and topographical characteristics , 2003, Clinical Neurophysiology.

[25]  U. Mitzdorf.  Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. , 1985, Physiological reviews.

[26]  F. H. Lopes da Silva,et al.  Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models. , 2004, Magnetic resonance imaging.

[27]  F Babiloni,et al.  Computerized processing of EEG-EOG-EMG artifacts for multi-centric studies in EEG oscillations and event-related potentials. , 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[28]  Fernando H. Lopes da Silva,et al.  Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models. , 2004 .

[29]  Giovanni Mirabella.  Volitional Inhibition and Brain–Machine Interfaces: A Mandatory Wedding , 2012, Front. Neuroeng..

[30]  B. Rockstroh,et al.  Increased Cortical Representation of the Fingers of the Left Hand in String Players , 1995, Science.

[31]  L. Cohen,et al.  Neuroplasticity in the context of motor rehabilitation after stroke , 2011, Nature Reviews Neurology.

[32]  Tomohiro Kizuka,et al.  Motor imagery and electrical stimulation reproduce corticospinal excitability at levels similar to voluntary muscle contraction , 2014, Journal of NeuroEngineering and Rehabilitation.

[33]  Olivier Lambercy,et al.  Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot , 2014, Journal of NeuroEngineering and Rehabilitation.

[34]  K. K. Tan,et al.  The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy. , 1993, Electroencephalography and clinical neurophysiology.

[35]  Andrés Úbeda,et al.  SVM-based Brain-Machine Interface for controlling a robot arm through four mental tasks , 2015, Neurocomputing.

[36]  Robert Riener,et al.  Control strategies for active lower extremity prosthetics and orthotics: a review , 2015, Journal of NeuroEngineering and Rehabilitation.

[37]  Giovanni Mirabella,et al.  Should I stay or should I go? Conceptual underpinnings of goal-directed actions , 2014, Front. Syst. Neurosci..

[38]  Febo Cincotti,et al.  High-resolution EEG techniques for brain–computer interface applications , 2008, Journal of Neuroscience Methods.

[39]  Narayanan Srinivasan,et al.  Cognitive neuroscience of creativity: EEG based approaches. , 2007, Methods.

[40]  Cuntai Guan,et al.  Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke , 2014, Front. Neuroeng..

[41]  M. Lotze,et al.  Motor imagery , 2006, Journal of Physiology-Paris.

[42]  G. Pfurtscheller,et al.  A fully automated correction method of EOG artifacts in EEG recordings , 2007, Clinical Neurophysiology.

[43]  B. Rockstroh,et al.  Removal of ocular artifacts from the EEG--a biophysical approach to the EOG. , 1985, Electroencephalography and clinical neurophysiology.

[44]  José del R. Millán,et al.  Brain-Controlled Wheelchairs: A Robotic Architecture , 2013, IEEE Robotics & Automation Magazine.

[45]  F. Lacquaniti,et al.  From Spinal Central Pattern Generators to Cortical Network: Integrated BCI for Walking Rehabilitation , 2012, Neural plasticity.

[46]  J. Nielsen,et al.  The effect of transcranial magnetic stimulation and peripheral nerve stimulation on corticomuscular coherence in humans , 2004, The Journal of physiology.

[47]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[48]  Javier Minguez,et al.  A Telepresence Mobile Robot Controlled With a Noninvasive Brain–Computer Interface , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[49]  Christopher J. James,et al.  Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data , 2012, Signal Process..