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Upper Limb EMG Artifact Rejection in Motor Sensitive BC~s Ander Ramos Murguialday*, Member, IEEE, Ernesto Soares*, and Niels Birbaumer Abstract- Motor imagery-based brain computer interface of the mu rhythm-based BCI studies, controlling for EMG (BCI) technology has motor rehabilitation as one of its main activations was not very common if not completely absent. fields of application. The use of a BCI as a neuroprosthetic for Most of the groups assumed no muscle activity while paralyzed limb motor restoration implies normally absence of performing motor imagery [1]. Later on, some EMG muscle activity. It is still an open question whether residual electrodes started to be placed on the limbs related to the motor activity in healthy individuals or in patients causes a moriagyduto bsvton fsalmve ns bias in the modulation of a motor imagery-based BCI control moriagyduto bsvton fsalmve ns signal. Although the influence of electromyographical (EMG) produced by the subjects [23, 24]. Nevertheless commonly activity in neck and cranial muscles upon BCI has been used methods for removing EMG artifacts in EEG signals studied, not much has been said concerning the relevance Of are linear filtering [12], blind source separation and EMG activity arising from arm muscles. We therefore used a 'idpnetcmontalysIC [1]prcpe hand motor imagery-based BCI system paradigm designed for inendt component analysisIC [14], pn aee rnf rinciple A motor rehabilitation coupling a BCI with an online driven atfcmpeonetaalyi [15]o anoud weavelet trasoremsv [16.eA robotic orthosis to compare different EMG activity detection atfc-eoa ehdsol eal ormv h methods regarding their influence in the resulting analysis of artifacts as well as keep the related neurological neurophysiologlical signals. Fourteen healthy subjects phenomenon intact, Therefore, if sufficient data is acquired, underwent four sessions in which they were asked to perform trials with artifact presence should be compared to the motor imagery task alone (receiving no feedback), motor cleaned data to study the artifact neural correlates. imagery with (visual and proprioceptive) feedback, active Mcalnetl.[7dmosredhtbairyhsae movement, passive movement and rest. Six different EMG Mcnarlnaned eith al.ia [17] deosratedftacts buraing rhthms earey feature extraction methods were calculated and three different cnaiae ihcailEGatfcsdrn h al data time windows were used for muscle activity threshold training sessions. EMG activity was found in forearm definition. Three different electrode spatial distributions were extensor muscles during contralateral forearm muscle utilized for removingz the EMG artifacts: a) coming from all activity and while performing mental task not related to the electrodes on the arms, b) just the ones placed on the finger movements [ 19], similar to Whitham et. al findings in imagery, side and c) just the ones on the healthy arm We aaye ujcswt igelm xlddfo compared the different EMG rejection methods by calculating palye sujcswt asigeim exuddfo the number of trials deemed artifact-free by each method. In Parayi [2],hysgetdta M ciaindrn this paper we demonstrate that different EMG artifact mental activity may be related to evolutionarily important removal methods lead to distinct partitions of the total mechanisms for fight or flight namely mental activity may available data, thus yielding different influence of the method constitutively activate protective programs of preparedness used to remove EMG artifacts on task related artifacts for action [20,21]. Nevertheless no research group has regarding number of trials contaminated and the differences in performed an exhaustive study of the influence of arm trias rjectd uing he iffeentmethds.muscles contractions on the EEG recorded activity. On the 1. INTRODUCTION other hand, most of the EMG artifact rejection algorithms JN the motor imagery BCI community the motor task using recorded muscle activity, use very simple features like ~related EMG artifact rejection has not been historically a RMS, variance, or rectified signals [11, 23, 24], to detect topi of itrs. Th minima cotato of muscle muscle activity. Furhermore, these methods normally use a

Upper Limb EMG Artifact Rejection in Motor Sensitive

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Upper Limb EMG Artifact Rejection in Motor Sensitive

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  • Upper Limb EMG Artifact Rejection in Motor Sensitive BC~s

    Ander Ramos Murguialday*, Member, IEEE, Ernesto Soares*, and Niels Birbaumer

    Abstract- Motor imagery-based brain computer interface of the mu rhythm-based BCI studies, controlling for EMG(BCI) technology has motor rehabilitation as one of its main activations was not very common if not completely absent.fields of application. The use of a BCI as a neuroprosthetic for Most of the groups assumed no muscle activity whileparalyzed limb motor restoration implies normally absence of performing motor imagery [1]. Later on, some EMGmuscle activity. It is still an open question whether residual electrodes started to be placed on the limbs related to themotor activity in healthy individuals or in patients causes a moriagyduto bsvton fsalmve nsbias in the modulation of a motor imagery-based BCI control moriagyduto bsvton fsalmve nssignal. Although the influence of electromyographical (EMG) produced by the subjects [23, 24]. Nevertheless commonlyactivity in neck and cranial muscles upon BCI has been used methods for removing EMG artifacts in EEG signalsstudied, not much has been said concerning the relevance Of are linear filtering [12], blind source separation andEMG activity arising from arm muscles. We therefore used a 'idpnetcmontalysIC [1]prcpehand motor imagery-based BCI system paradigm designed for inendt component analysisIC [14], pn aee rnf rinciple Amotor rehabilitation coupling a BCI with an online driven atfcmpeonetaalyi [15]o anoud weavelet trasoremsv [16.eArobotic orthosis to compare different EMG activity detection atfc-eoa ehdsol eal ormv hmethods regarding their influence in the resulting analysis of artifacts as well as keep the related neurologicalneurophysiologlical signals. Fourteen healthy subjects phenomenon intact, Therefore, if sufficient data is acquired,underwent four sessions in which they were asked to perform trials with artifact presence should be compared to themotor imagery task alone (receiving no feedback), motor cleaned data to study the artifact neural correlates.imagery with (visual and proprioceptive) feedback, active Mcalnetl.[7dmosredhtbairyhsaemovement, passive movement and rest. Six different EMG Mcnarlnaned eith al.ia [17] deosratedftacts buraing rhthms eareyfeature extraction methods were calculated and three different cnaiae ihcailEGatfcsdrn h aldata time windows were used for muscle activity threshold training sessions. EMG activity was found in forearmdefinition. Three different electrode spatial distributions were extensor muscles during contralateral forearm muscleutilized for removingz the EMG artifacts: a) coming from all activity and while performing mental task not related tothe electrodes on the arms, b) just the ones placed on the finger movements [ 19], similar to Whitham et. al findings inimagery, side and c) just the ones on the healthy arm We aaye ujcswt igelm xlddfocompared the different EMG rejection methods by calculating palye sujcswt asigeim exuddfothe number of trials deemed artifact-free by each method. In Parayi [2],hysgetdta M ciaindrnthis paper we demonstrate that different EMG artifact mental activity may be related to evolutionarily importantremoval methods lead to distinct partitions of the total mechanisms for fight or flight namely mental activity mayavailable data, thus yielding different influence of the method constitutively activate protective programs of preparednessused to remove EMG artifacts on task related artifacts for action [20,21]. Nevertheless no research group hasregarding number of trials contaminated and the differences in performed an exhaustive study of the influence of arm

    trias rjectd uing he iffeentmethds.muscles contractions on the EEG recorded activity. On the1. INTRODUCTION other hand, most of the EMG artifact rejection algorithms

    JN the motor imagery BCI community the motor task using recorded muscle activity, use very simple features like~related EMG artifact rejection has not been historically a RMS, variance, or rectified signals [11, 23, 24], to detect

    topi of itrs. Th minima cotato of muscle muscle activity. Furhermore, these methods normally use a

  • These results indicate that due to very little muscle B. Data Acquisitioncontraction without generating actual movement, brain BEG data were acquired using a BrainAmp 128-channelactivity similar to the one elicited through motor imagery amplifier from Brainproducts GmibH, Munich Germany. Ancan be evoked and used for BCI. These ideas suggest that BasyCap 128-channel BEG cap (modified 10-20 system)BCI paradigms where muscle activity is not desired could from the same company was used for BEG data acquisition;be easily biased by these little muscle contractions. All this referenced to the nose, and grounded anteriorly to Fz. Intogether suggests that the influence of these little muscle order to record eye movements, we measured horizontalcontractions while performing motor imagery and not BOG on both eyes and vertical BOG on the right eye. Themuscle activity related tasks in general might have an Brainamp, amplifier and signal processing module wereimportant relevance in the BCI. connected through a client-server architecture, with the

    In this work we coupled a mu rhythm based BCI on-line amplifier acting as the server and the signal processingwith a robotic hand orthosis and 12 healthy right handed module running on a stand-alone client PC. Data werevolunteers were asked to perform motor imagery alone with sampled at 500 Hz and transmitted over a TCP/IP protocolno feedback, motor imagery being the movement of the to the client PC for storage and real-time signal processingorthosis the feedback, passive movement, active movement using the BC12000 platform (www.bci2OOO.org) [22]).and rest. We performed BMG correction of the data to BMG data was acquired using 8 bipolar AgIAgCl electrodesdetect trials with unwanted BMG activity using the different from Myotronics-Noromed (Tukwila, WA, USA) andrejection methods. We wanted to study how the BMG placed on antagonistic muscle pairs; one close to theactivity and resulting feedback to cortical sensory-motor external epicondyle on the extensor digitorum. (fore armareas could affect the neural activity used in the BCI by extensor), other on the flexor carpi radialis (fore arm flexor)introducing a bias in the frequency analysis. In this paper other on the external head of the biceps (upper arm flexor)we compare the different traditionally used methods with and the last one placed on the external head of the tricepsmore accurate methods in BMG activity detection. (upper arm extensor). The BMG electrodes impedance was

    always kept under 20 Ohm.II. METOS

    C. OrthosisA. FExperimental Setup Bach finger is moved individually using a DC Motor M-12 right handed subjects were asked to perform 5 different 28 from Kaehlig Antriebstechnik GmbH, Hannover,tasks following the randomly presented auditory cues: Germany with worm gearhead from the same company formotor imagery task related to the hand fixed to the orthosis each finger. This motor drives via cogwheel and cograil a(Classi); driving the orthosis with a hand-related motor bowden cable. A finger holder is mounted on the other sideimagery task (Class2); passive movement of the hand of each bowden cable. As a physical connection between(Class3); active movement (the subject opens and closes the orthosis and host computer, a serial connection was used.hand) (Class4); and a rest condition (Class5).Two seconds During the Motor imagery task related with feedback class,after hearing the corresponding "right hand" auditory cue a the BC12000 classifier output sends an output every 120"GO" cue was presented and the subject used the msec and 5 consecutive positives for the same class areappropriate kinesthetic hand motor imagery until an "end" needed in order to send the orthosis a no-move (zerocue was presented 5 seconds after the "GO" (See Fig. 1). velocity value) or a move (positive velocity) command.The subjects underwent 4 sessions with 4 runs each having D. Signal Processing)a total amount of 80 trials per subject. The subjects have TeEGatvt sdb h C ltomfo hnever performed any motor imagery experiment before. lkThe BE +ciiyU sdbheBpafomfo h

  • defined as the midpoint between the means of these 2 F. Artifact Rejectiondistributions, was adaptive to account for changes in the We performed a whole battery of artifact rejectionshapes of these distributions over the course of training. methods. The feature extraction was performed using the

    The EMG activity data was highpass filtered at 10 Hz Root Mean Square (RMS), Wave Length (WL), Varianceand rectified. We used a 200 msec sliding window with a (VAR), Mean Absolute Value (MAy), Willison Amplitude180 msec overlap to calculate the following 5 EMG time- (WA) and rectified data. Concerning the thresholddomain features: Root Mean Square (RMS), Variance definition, 3 different methods were used: A) The first(VAR), Mean Absolute Value (MAV), Wave length (WL), rejection threshold (RThA) was determined at 3 standardWillison Amplitude (WA). A sixth feature, Rectified Data deviations (SD) of the extracted feature values on the rest(RD), was also used, which did not necessitate windowing class (Class5) in which all the trials were previously cleanedthe data. The MAV displays a large increase in value at of artifacts rejecting all the trials that had at least oneonset and maintains fairly high values during muscle values higher than 2 standard deviations above the mean.contraction. The WL provides indicators for signal B) The second rejection Threshold (RThB) was defined at 3amplitude and frequency. WA represents the different SD calculated on the motor imagery class (Classl). C) Themuscle contraction levels. The VAR represents the EMG third rejection threshold (RThC) was calculated at 3 SDsignal power, helping to identify onset and contraction and during the inter trial interval (ITI) time and D) The lastthe RD represents the envelope of the muscle activity and rejection threshold (RThD) was at 3 SD using thetherefore the muscle overall contraction. instructions period to calculate it.

    1. L We performed 3 different rejection depending on theVAR =" X - i~k) electrodes used to detect artifacts, and a trial was rejected if

    k=1 during 250 msec the feature values extracted crossed the2. RMS= IL 2 pre defined threshold on: a) any of the 8 bipolar electrodes

    L L Xkplaced on both arms (BOTH), b) any of the 4 bipolark=1 electrodes placed on the arm attached to the orthosis (IPSI)

    3. AM y = L c't I and c) any of the 4 bipolar electrodes placed on the~ I contralateral arm relative to the arm attached to the orthosis

    L k1(CONTRA).4. WL jAXk I AXk xk X-x In all, we therefore used 72 EMG activity detection

    k=1 k - methods (6 feature x 4 periods for threshold calculation x 3L different sets of electrodes).

    5. WA k=1 Af xk -Xk+iI1) 11 ,u~with f (x) = 1 if x > threshold and 0 otherwise We compared the proportion of trials accepted as EMG

    activity-free, i.e. without EMG artifacts, relative to the totalE. Study Design number of trials using the 72 different detection methods.

    The screening session consisted of four runs with 75 Overall results, averaging across all session, grouping alltrials each. Auditory and visual cues were presented to the subjects and irrespective of the class, are presented insubjects. After a variable rest period of 5 to 7.5 sec, a Figure 2. Methods were ordered in ascending proportion ofbaseline period of 1 sec an instruction auditory and visual trials classified as EMG activity-free. Results in Figure 1cue was presented to the subject. In the screening session indicate that subjects displayed significant muscle activity

    thr wer 3 -clse (Rgh Hand Lef Han an Ret) wMhiepromn all ofP the difrntass.Frthroe

  • Prpotinofal tilscls~e a da b cifeer E~elcto nftt MtnS~)The WAMP feature was observed to be the most12 stringent, closely followed by VAR. The least number of

    trials were eliminated using the RD feature.The influence of the time period used to calculate the

    T threshold was then studied using the same procedure. Thesession-wide average performance of the 18 different

    0.6 methods (3 electrode locations x 6 features) is presented inT Figure 4.Using the Retperiod (R1XiW-t calcul~ateth

    -60.4 threshold yielded the least number of trials, while using the02 ITI (RThC) proved to be the less stringent method. These

    results indicate that during the Rest period subjects0 displayed the least muscle activity, thus yielding lowest

    thresholds (RThA) and, therefore, lowest proportion of10 20 303 0 t trials classified as EMG activity-free. The inverse applies to

    Figure 2 - Average proportion of the total number of18trials classified as EMIG activity-free using the different 0.STdetection methods. Error bars depict session-wide 06 NTstandard deviations. 0

    In order to evaluate the influence of the 3 differentvariables characterizing each method (Feature, Time periodused to calculate the threshold, Electrode Location),0.detection methods were separated according to the different 0.4levels of each variables and their average performance was 1.calculated across all sessions, subjects and task classes.

    By separating the methods according to the EMIG featureused, we evaluated the influence of the different EMG 0.1 7801 1 21 4101 71features in EMG activity detection. Results are presented inFigure 3, where for each EMG feature, the trial-wide Figure 4- Average proportion of the total number ofaverage proportion of EMIG activity-free trials is presented trials classified as EMG activity-free using differentfor each of the 12 classification methods (4 thresholds x 3 time periods to calculate the threshold used to detectelectrode locations). EMG activity.

    Proportion oftrialsclasslfed os undftv..4r MoraMG featw"o

    RMS Finally, we studied the influence of electrode location on'Wlthe proportion of trials deemed EMG-activity free. The-RAWRECT session-wide average performance of the 24 different

    methods (4 Time periods used to calculate the threshold x 6Y features) are presented in Figure 5. As was expected, using

    electrodes placed in both (BOTH) arms proved the moststringent method, followed by methods using the arm

    'A'\ *OOA Aattached to the orthosis (IPSI). Using electrodes on the

  • same afferent information as in a normal situation) closed- REFERENCESloop control systems tailored to the low-bandwidth nature [1] Fatourechi, M., Bashashati, A., Ward, R. K., and Birch, G. E., "BOGof BCI signals as a new and intuitive tool for motor and BMG Artifacts in Brain Interface Systems: a Survey", Clinicalrehabilitation could be benefit of this bias by reinforcing the Neurophysiology, Vol. 118, No.3, Mar 2007, pp.480-494EMG related brain activations. [2] B. Blankertz, K.R. Miler, G. Curio, T. M. Vaughan, G. Schalk, J. R.Wolpaw, A. Schlgl, C. Neuper, G. Pfurtscheller, T. Hinterberger, M.

    Pmwo ftatdsitda wWgWcvni fwtfdi Schrder, N. Birbaumer, "The BCI Competition 2003: Progress and-'Pa' Perspectives in Detection and Discrimination of BEG Single Trials,"

    0.0OTH~ IEEE transactions on biomedical engineering. Vol. XX, No Y, 2004.[3] A. Chatterjee, V. Aggarwal, A. Ramos, S. Acharya and N. V. Thakor

    08 (2007) A brain-computer interface with vibrotactile biofeedback for57 haptic information.In: J. NeuroEngineering and Rehabilitation

    1 2007,4:40 (17 Oct 2007).!E0.6http://www~jneuroengrehab.cono~content/4/l/40

    [4] A. Vuckovic, F. Sepulveda Quantification and visualisation of0 differences between two motor tasks based on energy density maps

    for rain-computer interface applications Clinical Neurophysiology,~0.4 Volume 119, Issue 2, Pages 446-458 (2007)

    0.3 [5] A. B. Schwartz, X. T. Cui, D. J. Weber, and D. W. Moran, "Brain-controlled interfaces: movement restoration with neural prosthetics,"

    02- Neuron, vol. 52, pp. 205-20, 2006.[6] T. Hinterberger, N. Neumann,, M. Pham, A. Kbler, A. Grether, N.

    01 I Hofmnayer, B. Wilhelm, H. Flor and N. Birbaumer, A multimodal1 2 3 4 5 6 7 8 10 11 12 13 14 55 16 1? 18 19 20 21 22 22 24

    Classifier brain-based feedback and communication system, ExperimentalBrain Research. 2004; 521-526 (154).

    Figure 5- Average proportion of the total number of [7] S. d Vries, T. Mulder. Motor imagery and stroke rehabilitation: atrias cassfie as MG ctiityfreeusig dffeent critical discussion, J Rehabil Med. 2007 Jan;39(1):5-13.trils lasifid a EM aciviy-fee sin difernt [8] B. T. Volpe,]L H. Krebs,, N. Hogan, L. Edelstein, C. Diels, M.

    electrode locations. Aisen.A novel approach to stroke rehabilitation: Robot-aidedsensorimotor stimulation Neurology 2000 54: 1938-1944.

    [9] R. Bos, S. deWaele, and P. M. T. Broersen, "Autoregressive spectralFor the control of prosthetic devices oriented to the estimation by application of the Burg algorithm to irregularly

    completely paralyzed individuals, has to be noticed that sampled data," IEEE Trans Instrum. Meas., vol. 5 1, pp. 1289, 2002.EMG activity in terms of "quasimovements" could vary the [10] N. Delorme and S. Makeig. BBGLAB:an open source toolbox, ofsingle-trial EEG dynamics including independent componentneural signals used by the BCI classifier when using healthy analysis, Journal of Neuroscience Methods (2004), vol. 134: 9-2 1.subjects as a control for the development of such BCs. [11] A. Vuckovic and F. Sepulveda. Quantification and visualisation ofThis data suggest that testing the 1CI platforms on healthy differences between two motor tasks based on energy density maps

    subjctscoul led t fale rsuls inters o accrac if for brain-computer interface applications. Clinical Neurophysiologysubjctscoud lad o flseresltsin erm ofaccrac if 119 (2008) 446-458the "quasimovements" are not wanted in that type of 1CI [12] J. S. Barlow, "EMG artifact minimization during clinical EEGapplication. A quantification of the potential bias of the BCI recordings by special analog filtering", Blectroencephaiogr. Clin.

    systm cusedby and oveent s nededto eterine Neurophysiol., vol. 58, no.2, pp. 161-174, Aug. 1984sysem ausd b had mveentis eedd t deermne [13] J. R. Ives and D. L. Schomer, "A 6-pole filter for improving thehow to remove or avoid this effect. In this paper we readability of muscle contaminated BEGs", Electroencephalogr.demonstrated that many BCI and upper limb motor control Clin. Neurophysiol., vol. 69, no.5, pp. 486-490, May.1988.studies that did not control for EMG artifacts could be [14] S Halder, M Bensch, J Mellinger, M Bogdan, A Kbler, N Birbaumer,and W Rosenstiel. Online Artifact Removal for Brain-Computerbiased by upper limb muscle activity and therefore not valid Interfaces Using Support Vector Machines and Blind Sourceas proof of concept design for completely paralyzed Separation. Comput Intell Neurosci. 2007; 2007: 82069patients. A more detailed analysis on the neural correlates [15] T. D. Lagerlund, F. W. Sharbrough and N. B. Busacker, "Spatialfiltering of multichannel electroencephalographic recordings throughrelated to the different EMG artifact removal methods principal component analysis by singular value decompnosition", J_

  • [20] Whitham EM, Pope KJ, Fitzgibbon SP, Lewis T, Clark CR, LovelessS, et al. Scalp electrical recording during paralysis: quantitativeevidence that EEG frequencies above 20 Hz are contaminated byEMG. Clin Neurophysiol 2007; 118:1877-88.

    [21] Whitham EM, Lewis T, Pope KJ, Fitzgibbon SP, Clark CR, LovelessS. DeLosAngeles D, Wallace AK, Broberg M, Willoughby JO;Thinking activates EMG in scalp electrical recordings ClinicalNeurophysiology 119 (2008) 1166-1175

    [22] G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, J.R.Wolpaw. BC12000: a general-purpose brain-computer interface(BCI) system. IEEE Trans Biomed Eng. 2004;5 1: 1034 -1043.

    [23] Wolpaw JR and McFarland,, Control of a two-dimensionalmovement by a noninvasive brain-computer interface in humans,PNAS 2004, vol( 0 1) no. 51 17849-17854

    [24] Birbaumner N, Breaking the Silence: Brain-computer interfaces (BCI)for communication and motor control, Psychophysiology (2006),Vol(43), 517-532