TMS Induced EEG Artifacts Analysis. Based on the Partial Cross-Correlations

Transcription

1 Contemporary Engneerng Scences, Vol. 7, 2014, no. 24, HIKARI Ltd, TMS Induced EEG Artfacts Analyss Based on the Partal Cross-Correlatons Unoo Lee Department of Electroncs Engneerng, Hallym Unversty 1 Hallymdaehak-gl, Chuncheon, Gangwon-do , Korea Woo-Kyoung Yoo Dept of Physcal Medcne and Rehabltaton, Hallym Unversty 1 Hallymdaehak-gl, Chuncheon, Gangwon-do , Korea Copyrght 2014 Unoo Lee and Woo-Kyoung Yoo. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. Abstract Combnng Transcranal magnetc stmulaton (TMS) and Electroencephaloraphy (EEG) offered nsghts nto neural nteracton durng cognton whch allows the nvestgaton on the causal role of specfc bran areas n behavor and the nteractve connecton between the bran areas. The TMS pulse n the TMS-EEG combnng study ntroduces artfacts n the EEG electrodes whch may mask the underlyng neural actvty lastng about 5 through even hundreds mllseconds after the pulse. In ths study a statstcal method on the removal of TMS nduced EEG artfacts s presented based on partal cross-correlatons. The method yelds estmates of lnear correlatons between components obtaned by Independent Component Analyss (ICA) and TMS sgnal that are not affected by lnear correlatons wth other artfacts. We used the EEG data obtaned from four healthy subects wthout any neurologcal dsorder, who were recevng sngle pulse TMS-EEG and sham-eeg stmulus on the left Broca s area. ICA flters traned on the reduced verson of 60 channel EEG data collected durng sngle pulse

2 1426 Unoo Lee and Woo-Kyoung Yo TMS-EEG and sham-eeg recordngs. It dentfed the reduced number of statstcally ndependent source channels, n whch the ICA components orgnatng from the TMS-nduced artfact are classfed by estmatng the partal cross-correlaton coeffcents between ICA components of sngle pulse TMS-EEG and sham-eeg stmulus after ICA decomposton. The results showed whch ICA components are related to TMS nduced EEG artfacts, suggestng the effcency and the relablty of the method developed n ths study. Keywords: TMS-EEG, artfacts, ICA, partal cross-correlatons, bran 1 Introducton Transcranal magnetc stmulaton (TMS) can be used to actvate or deactvate a specfc part of the bran non-nvasvely causng depolarzaton or hyperpolarzaton n cortcal neurons by the electromagnetc nducton. It s wdely used to measure the connectvty of the prmary motor cortex and a muscle to evaluate bran damage and other dsorders related to the motor cortex [1] and also used to study the effects of phosphenes by stmulatng the prmary vsual cortex [2] and the speech processng by dsruptng momentarly the Broca s area [3]. TMS s dvded nto two types, sngle or pared pulse TMS and repettve TMS dependng on the stmulaton mode. Sngle pulse TMS causes neurons n the neocortex under the ste of stmulaton to depolarze and dscharge an acton potental. Repettve TMS can produce longer-lastng effects such as changes n synaptc effcacy by ncreasng or decreasng the exctablty of the cortcospnal dependng on the ntensty of stmulaton, col orentaton, and frequency. EEG, a non-nvasve technque records spontaneous electrcal actvty of the bran by measurng the voltage fluctuatons resultng from onc current flows wthn the neurons of the bran [4]. EEG s used to clncally observe the type of neuronal oscllatons and therefore can be used to dagnose Epleptc actvty, bran death, or sleep dsorders from ther abnormal patterns of the EEG sgnals [5-6]. Furthermore, nformaton on functonal connectvty between the cortcal areas nvolved n dfferent cogntve tasks and processes can be extracted from the spectral propertes and the coheson of the spontaneous oscllatons of EEG n dfferent bran area by flterng and Fourer transformaton. Combnng TMS and EEG offered nsghts nto neural nteracton durng cognton whch allows the nvestgaton on the causal role of specfc bran areas n behavor and the nteractve connecton between the bran areas [5-6]. EEG sgnals are typcally contamnated wth bologcal and envronmental artfacts. Bologcal artfacts nclude eye-nduced artfacts, cardac artfacts, muscle actvaton-nduced artfacts and glossoknetc artfacts [7]. Envronmental artfacts nclude electrode spkes orgnatng from a momentary change n the

3 TMS nduced EEG artfacts analyss 1427 mpedance of a gven electrode due to body movement or settlng of the electrodes, and 50 or 60 Hz lne nose due to poor groundng of the EEG electrodes [8]. The TMS pulse n the TMS-EEG combnng study ntroduces artfacts n the EEG electrodes. The magnetc pulse of TMS affects the muscles and motor nerves underneath the col causng the muscle actvaton and eye movement, whch results n the nducton of EEG artfacts. The magnetc pulse of TMS can also excte the somatosensory nerve endngs and the col clck actvates the audtory system of the subect, whch can be seen n EEG as audtory or somatosensory evoked potentals [9]. Independent Component Analyss (ICA) s applcable to separate EEG data nto neural actvty and artfact [10]. Many methods usng ICA are appled to hgh-densty EEG data to reect components of artfact [10-11]. However, there are few of them whch resolve TMS nduced artfact removal problem on EEG. In ths study, a statstcal method on the removal of TMS nduced EEG artfacts s presented based on partal cross-correlatons. The method yelds estmates of lnear correlatons between components obtaned by ICA and TMS sgnal that are not affected by lnear correlatons wth other artfacts. The EEG data used n ths study were obtaned from four healthy subects wthout any neurologcal dsorder, who were recevng sngle pulse TMS-EEG and sham-eeg stmulus on the left Broca s area. ICA flters traned on the reduced verson of 60 channel EEG data collected durng sngle pulse TMS-EEG and sham-eeg recordngs. The ICA components orgnatng from the TMS-nduced artfact are classfed by estmatng the partal cross-correlaton coeffcents between ICA components of sngle pulse TMS-EEG and sham-eeg stmulus after ICA decomposton. The results showed whch ICA components are related to TMS nduced EEG artfacts, suggestng the effcency and the relablty of the method developed n ths study. 2 Methods for TMS nduced EEG artfact detecton and removal A total of 4 healthy male subects (L1, L3, L5 and L9 wth ages: ) wthout any neurologcal dsorder were selected n ths study. Subects were screened wth the TMS Screenng Questonnare. Once nformed consent was obtaned, the ncluson and excluson crtera were revewed. Baselne measures ncluded the modfed Ednburgh Handedness Inventory and a baselne Mnmental State Exam (MMSE). Subects vsted 2 tmes separately for the TMS-EEG study vst wth at least two days nterval. One of the two TMS-EEG study vsts were for sngle pulse TMS-EEG, the other were for sham-eeg. Subects were ncluded n ether the sham-eeg frst or the sngle pulse TMS-EEG frst group by random assgnment by usng a random number generaton method. 60 Channel EEG were recorded at

4 1428 Unoo Lee and Woo-Kyoung Yo a total of 4 TMS-EEG and 4 sham-eeg data. The sngle pulse TMS stmulus was treated on the left Broca s area. Trans of sngle TMS pulses were admnstrated at ntervals of 300 ms for a 1 mnute perod at 80% of actve motor threshold on left Broca s area (area 44) for the sngle pulse TMS-EEG group. Sham-EEG underwent the same procedure for dentfyng stmulus locaton used n subects recevng sngle pulse TMS-EEG. Smulated TMS were admnstered usng Magstm Placebo 70 mm fgure-of-8 shaped cols producng dscharge nose and vbraton smlar to a real 70 mm col wthout stmulatng the cerebral cortex. The electrcal stmulaton of the scalp, whch s nduced n the sngle pulse TMS-EEG experment, was smulated by attachng surface electrodes underneath the sham col and n contact wth the scalp and usng Nerve Conducton Study devces routnely to admnster electrcal shocks to the scalp smultaneous to each smulated TMS tran. Fgure 1 represents the flowchart of the overall processes of ths method. EEG data were sampled prelmnary at 1.45 khz and then re-sampled at khz before flterng. The EEG data ntally referenced to ste Fz whch s the 14 th channel n ths study. The referenced EEG data were fltered offlne usng a bandpass flter between 1 Hz and 95 Hz wth a notch at 50 Hz to remove envronmental noses. Next, artfactual channels are statstcally classfed, removed and nterpolated based on ther dstrbutons and correlatons to each other. The data were referenced to the average of all scalp electrodes. Then, ICA was performed wth FastICA algorthm on the data and the decomposed components were analyzed for bologcal artfacts and TMS nduced artfact usng partal cross-correlatons. Fnally, the ICA components orgnatng from bologcal artfacts and TMS-nduced artfact were subtracted from each channel data. Fg. 1. The flowchart of the overall processes of ths method

5 TMS nduced EEG artfacts analyss 1429 Artfactural channels are statstcally classfed by estmatng the medan M ( r x, x ) of the Pearson correlaton coeffcents r x, x of each channel x to other channel, x, 1,, n. A contamnated channel usually has low correlaton wth other channels. A channel s removed and then reconstructed by nterpolatng wth neghborng channels f the medan M( r x, x ) of t les out of 99% confdence nterval range. The nterpolaton was performed by usng EEGLAB sphercal splne nterpolaton functon. The correlaton r x, x between two channels x and x can be estmated by Eq. (1). r x, x ( x x )( x x ) (1) Where for x. x s the average of x and s ts standard devaton and lkewse The mathematcal model of ICA s descrbed as the followng equaton, x As where x s the random vector whose elements are the mxture of x 1,, x, s s the random vector wth elements n s 1,,sn and A s the matrx wth elements. The components can be classfed to sgnals from bran a, actvtes and artfacts nduced by TMS stmulaton as well as those by body movements or electrode msmatchng. The partal correlaton coeffcent r s s s, / between ICA components s and s s defned as Eq. (2), n whch the lnear effect of s the sum of sources accountng for typcal EEG artfacts s to be removed and r s, s, r s s, and r s, s are cross-correlaton coeffcents. r s, s / s rs, s rs, s r s, s (2) 2 2 (1 r )(1 r ) s, s Lnear correlatons between s and s and between s and s are removed from r s, s by subtracton, and the dfference s normalzed by the geometrc average of the devatons of these correlatons from one. Ths s conceptually smlar to estmatng the correlaton between s and s, whereas s s held constant expermentally. The ICA component of TMS nduced EEG artfact s statstcally classfed by estmatng the medan M ( r s, s / s ) of the partal correlaton coeffcents r s s s, / of each ICA source s to others, s, 1,, m wthn 99% confdence nterval lmt crtera, where m s the total number of ICA components after typcal EEG artfact removal. s, s

6 1430 Unoo Lee and Woo-Kyoung Yo 3 Results and Dscussons Fgure 2 (a) and (b) show the tme seres of the sngle pulse TMS-EEG and sham-eeg data, respectvely, of subect L5 wth khz of re-samplng rate before (top) and after (bottom) bandpass (1~95Hz) and notch ( 50 3Hz) flterng, where the x axs represents tme (sec) and y axs, voltage (μv). Fgure 3 (a) and (b) show the power spectra of the sngle pulse TMS-EEG and sham-eeg data, respectvely of subect L5 wth khz of re-samplng rate after bandpass (1~95Hz) and notch ( 503Hz) flterng and nterpolatng artfactural channels, where the channel maps for frequences of 2.1, 5.7, 9.2, 19.8, and 39.6 Hz are shown for each power spectrum and the x and y axes represent the frequency (Hz) and the power ntensty (10log10 μv 2 /Hz), respectvely. The TMS-nduced artfacts are notceable at fgure 3 showng long lastng dsrupton n the EEG data. The lnear trends and 50 Hz lne noses are removed from EEG data as shown n fgure 2 (b) and 3 (b). Fgure 4 (a) and (b) represent the tme courses of channels for sngle pulse TMS-EEG data and sham-eeg data, respectvely after ICA decomposton analyss (top) and TMS nduced EEG artfact removal (bottom) based on the partal cross-correlatons. The TMS-nduced artfacts of TMS-EEG data are shown to be removed n the bottom of fgure 5.represents the tme courses of channels for sngle pulse TMS-EEG data and sham-eeg data, respectvely after ICA decomposton analyss (top) and TMS nduced EEG artfact removal (bottom) based on the partal cross-correlatons.1 represents the flowchart of the overall processes of ths method. EEG data were sampled prelmnary at 1.45 khz and then re-sampled at khz before flterng Fg. 2. The tme seres of (a) the sngle pulse TMS-EEG and (b) sham-eeg data, of subect L5 wth khz of re-samplng rate before (top) and after (bottom) bandpass (1~95Hz) and notch ( 50 3Hz) flterng

7 TMS nduced EEG artfacts analyss 1431 Fg. 3. The power spectra of the sngle pulse TMS-EEG and sham-eeg data, respectvely of subect L5 wth khz of re-samplng rate after bandpass (1~95Hz) and notch ( 50 3Hz) flterng and nterpolatng artfactural channels Fg. 4. the tme courses of channels for sngle pulse TMS-EEG data and sham-eeg data, respectvely after ICA decomposton analyss (top) and TMS nduced EEG artfact removal (bottom) based on the partal cross-correlaton. 4 Conclusons In ths study, a statstcal method on the removal of TMS nduced EEG artfacts s presented based on partal cross-correlatons. The method yelds estmates of lnear correlatons between components obtaned by ICA and TMS sgnal that are not affected by lnear correlatons wth other artfacts. The EEG data used n ths study were obtaned from four healthy subects wthout any neurologcal dsorder, who were recevng sngle pulse TMS-EEG and sham-eeg stmulus on the left Broca s area. ICA flters traned on the reduced verson of 60

8 1432 Unoo Lee and Woo-Kyoung Yo channel EEG data collected durng sngle pulse TMS-EEG and sham-eeg recordngs. The ICA components orgnatng from the TMS-nduced artfact are classfed by estmatng the partal cross-correlaton coeffcents between ICA components of sngle pulse TMS-EEG and sham-eeg stmulus after ICA decomposton. The results showed whch ICA components are related to TMS nduced EEG artfacts, suggestng the effcency and the relablty of the method developed n ths study. Acknowledgements. Ths research was supported by Basc Scence Research Program through the Natonal Research Foundaton of Korea (NRF) funded by the Mnstry of Educaton, Scence and Technology ( ). References [1] J. J. Borckardt, S. T. Reeves, W. Beam, M. P. Jensen, R. H. Gracely, S. Katz, A. R. Smth, A. Madan, D. Patterson and M. S. George: A Randomzed, Controlled Investgaton of Motor Cortex Transcranal Magnetc Stmulaton (TMS) Effects on Quanttatve Sensory Measures n Healthy Adults: Evaluaton of TMS Devce Parameters. Cln J Pan 27-6, pp (2011) [2] E.C. Caparell, W. Backus, F. Telang, G. J. Wang, T. Maloney, R. Z. Goldsten, D. Anschel and F. Henn: Smultaneous TMS-fMRI of the Vsual Cortex Reveals Functonal Network, Even n Absence of Phosphene Sensaton. The Open Neuromagng Journal, 4 pp (2010) [3] E. Clerget, A. Wnderckx, L. Fadga and E. Olver: Role of Broca s area n encodeng sequental human actons: a vrtual leson study. NeuroReport. 20 pp (2009) [4] B. Platt, B. Drever, D. Koss, S. Stoppelkamp, A. Plano, A. Utan, G. Merrck, D. Ryan, V. Mels, H. Wan, M. Mngarell, E. Porcu, L. Scrocch, A. Welch and G. Redel: Abnormal cognton, sleep, EEG and bran metabolsm n a novel knock-n Alzhemer mouse, PLB1. Plos One, 6-11 p. e27068 (2011) [5] C. Mnuss and G. Thut: Combnng TMS and EEG Offers New Prospects n Cogntve Neuroscence. Bran Topogr. 22 pp (2010) [6] N. C. Roqasch and P. B. Ftzqerald: Assessng cortcal network propertes usng TMS-EEG. Hum Bran Mapp pp (2013)

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