Casson, Alexander J.

Kohli, Siddharth

English

Conventional EEG (electroencephalography) has relied on wet electrodes which require conductive gel to help the electrodes make contact with the scalp. In recent years many dry electrode EEG systems have become available that do not require this gel. As a result they are quicker and easier to set up, with the potential to record the the EEG in situations and environments where it has not previously been possible. This paper investigates the practicality of using dry EEG in new nonconventional recording situations. In particular it uses a dry EEG recording system to monitor the EEG while a subject is riding an exercise bike. The results show that good-quality EEG, free from high-amplitude motion artefacts, can be collected in this challenging motion rich environment. In the frequency domain a peak of activity is seen over the motor cortex (C4) at 23 Hz starting five minutes after the start of the exercise task, giving initial insights into the on-going operation of the brain during exercise

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The University of Manchester ResearchTowards out-of-the-lab EEG in uncontrolled environments:feasibility study of dry EEG recordings during exercisebike ridingLink to publication record in Manchester Research ExplorerCitation for published version (APA):Kohli, S., & Casson, A. J. (2015). Towards out-of-the-lab EEG in uncontrolled environments: feasibility study of dryEEG recordings during exercise bike riding. In host publication IEEE.Published in:host publicationCiting this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. If citing, it is advised that you check and use thepublisher's definitive version.General rightsCopyright and moral rights for the publications made accessible in the Research Explorer are retained by theauthors and/or other copyright owners and it is a condition of accessing publications that users recognise andabide by the legal requirements associated with these rights.Takedown policyIf you believe that this document breaches copyright please refer to the University of Manchester’s TakedownProcedures [http://man.ac.uk/04Y6Bo] or contact uml.scholarlycommunications@manchester.ac.uk providingrelevant details, so we can investigate your claim.Download date:09. Jun. 2022Towards out-of-the-lab EEG in uncontrolled environments: feasibilitystudy of dry EEG recordings during exercise bike ridingSiddharth Kohli and Alexander J. Casson, Member, IEEEAbstract— Conventional EEG (electroencephalography) hasrelied on wet electrodes which require conductive gel to helpthe electrodes make contact with the scalp. In recent years manydry electrode EEG systems have become available that do notrequire this gel. As a result they are quicker and easier to setup, with the potential to record the the EEG in situations andenvironments where it has not previously been possible. Thispaper investigates the practicality of using dry EEG in new non-conventional recording situations. In particular it uses a dryEEG recording system to monitor the EEG while a subject isriding an exercise bike. The results show that good-quality EEG,free from high-amplitude motion artefacts, can be collectedin this challenging motion rich environment. In the frequencydomain a peak of activity is seen over the motor cortex (C4) at23 Hz starting five minutes after the start of the exercise task,giving initial insights into the on-going operation of the brainduring exercise.I. INTRODUCTIONSince its discovery in 1929 [1] EEG (electroencephalog-raphy) has become an invaluable tool for the non-invasivemonitoring of the human brain. Based upon placing smallmetal electrodes on the scalp it provides a high temporalresolution (but low spatial resolution) approach compared toother brain monitoring modalities such as fMRI (functionalMagnetic Resonance Imaging) and PET (Positron EmissionTomography).In addition, advances in electronics and miniaturizationmean that EEG is ideal for portable brain monitoring [2].A major limitation to this portability has always been theelectrodes used to connect the EEG recorder to the scalp.Sintered Ag/AgCl electrodes offer the best recording perfor-mance [3], [4], but are wet: they require a conductive gelto make a connection between the scalp and electrode; inaddition to needing standard preparations such as parting thehair and cleaning the scalp.As a result, conventional wet electrode EEG systems arevery difficult to set up quickly and without the assistance of atrained technician. In recent years there has been substantialprogress on dry EEG electrodes to mitigate these issues.These do not require conductive gel and can still collect highquality EEG signals; see [5] for a review of the electrodetechnologies avaiable. Such dry electrode EEG systems arenow starting to be commercially available from companiessuch as g.tec, Mindo, Quasar and others [6].To date, many studies on dry EEG electrodes have in-vestigated the comparison of wet and dry electrodes byThe authors are with the School of Electrical and ElectronicEngineering, The University of Manchester, UK. Email:siddharth.kohli@postgrad.manchester.ac.uk,alex.casson@manchester.ac.uk.reporting a correlation coefficient between EEG recordedat nearby locations with the two electrode types. Typicalvalues reported are: >0.93 [7]; 0.89 [8]; 0.83 [9]; 0.81–0.98[10]; 0.68–0.90 [11]; 0.39–0.85 [12]. However, in additionto being quicker and easier to set up, these novel electrodesalso offer the potential to record the EEG in situations andenvironments where it has not previously been possible: amajor aim of dry EEG systems is to open new avenuesin where and when EEG monitoring can be performed.To complement the above wet/dry comparison studies it isthus now essential to demonstrate the applicability of dryelectrode recording systems in new recording situations.This paper investigates this potential of using new dryelectrodes to record the EEG in non-conventional environ-ments. Rather than performing a comparison of wet and dryelectrodes, we make use of only dry electrodes and applythem in a situation where conventional EEG monitoring isnot generally possible: to record brainwave signals from asubject while pedalling on an exercise bike. This is an activemotion task with the potential for lots of motion interference.Our results demonstrate that EEG signals can be collectedin this situation, free from high-amplitude motion artefacts.Section II outlines the experimental protocol used, with theresults presented in Section III. Further, the collected EEGdata shows increased activity over the motor cortex (C4)in the mid-β band (23 Hz region) after five minutes ofbike riding activity. The implications of this are discussedin Section IV. Overall the results demonstrate the potentialof emerging dry EEG systems for providing high time res-olution brain monitoring in previously infeasible recordingscenarios.II. METHODSThree dry EEG recordings were carried out using theQuasar DSI-mini EEG recorder illustrated in Fig. 1. Thisis a six channel, 16-bit, 300 Hz sampling rate EEG recorderwhich uses fingered electrodes in order to penetrate the hairand obtain a direct connection with the scalp without havingto part the hair in advance. This is combined with an ultra-high input impedance Common-Mode Follower approach [8]which allows EEG recordings to be performed even in thepresence of a high impedance electrode connection (as noconductive gel is used to lower the impedance).The DSI-mini EEG unit was set up and placed on thesubject’s head by a technician as in standard EEG recordings.(The potential for the subject to self-don the EEG unit wasnot investigated in this study.) EEG electrodes were placedin 10/20 positions at F7, F8, C3, C4, T3, T4 with the groundFig. 1. The Quasar DSI-mini EEG recorder used in this experiment. Thisuses dry electrodes which are fingered to easily penetrate the hair and obtaina direct connection with the scalp.connection at A2 and the common mode follower input at Fz.The subject was then asked to pedal on a standard exercisebike set at minimum resistance for a period of one hour.At all times the subject was in the standard sitting positionfor riding a bike and their upper body movement was notrestrained in any way. Data was recorded at Imperial CollegeLondon and the institution’s Ethical Review Board approvedall experimental procedures involving human subjects.In Section III time domain analysis of the signals isperformed by visual inspection for the presence of motionartefacts in the collected raw EEG signals. For further anal-ysis the collected signals were bandlimited from 1–50 Hz,with an additional 50 Hz notch filter to remove mains noise.Frequency domain analysis is performed by segmentingthe EEG into five minute non-overlapping epochs. Withineach epoch the Welch transform is calculated using a 2 swindow of data, with 50% overlap present between eachwindow. A 211 point FFT with a Hamming window is usedas the basis for this transform. This approach allows thechanges in EEG power between each five minute epochto be investigated, whilst also giving multiple 2 s analysiswindows that can be averaged together in order to improvethe frequency domain signal-to-noise ratio and highlight theEEG frequencies that are consistently present during eachfive minute period.III. RESULTSFig. 2 shows the unfiltered EEG collected during one ofthe pedalling trials. The EEG data is almost completely freeof high-amplitude motion artefact in all of the channels forapproximately the first half an hour. After this the data qual-ity begins to degrade, most likely due to a build up of sweaton the scalp altering the electro-chemical equilibrium of theelectrode connection, as is common in EEG recordings [13].Air conditioning normally largely eliminates the presenceof such artefacts in non-mobile subjects [14], but this is ofcourse not possible in the active exercise task being carriedout here.From all of the EEG trials at least 20 minutes of goodquality EEG was collected. Fig. 3 shows a shorter section ofchannel C4 from three EEG recordings, illustrating the typi-cal EEG produced after bandlimiting filtering. The potentialto record low amplitude free-running EEG signals is clearlydemonstrated, despite the fact that the subject is pedalling−200002000T4EEG / μVC4C3 T30 1000 3000F8Time / sF7−200002000EEG / μV−200002000EEG / μV0 1000 3000Time / sFig. 2. The full EEG time series from one bike riding session; no filtering is applied. Good quality EEG data is collected in the first half an hour ofrecording, with very few high-amplitude motion artefacts present despite the ongoing bike riding activity.−10010EEG test 11195 1196 1197 1198 1199 1200EEG test3Time / sEEG test 2EEG / µV−10010EEG / µV−10010EEG / µVEEG test 3Fig. 3. Five seconds of EEG data from channel C4 in three seperaterecordings. This illustrates the typical quality of EEG recorded after 20minutes of bike riding activity.on a bike during the collection of these signals and has beendoing so for 20 minutes.To demonstrate that these signals contain potentially usefulneurophysiological information, Fig. 4 presents the EEGPower Spectral Densities (PSD) for the first 20 minutes ofrecording (while good quality EEG data is being collectedin all of the cases). For illustration purposes only the resultsfor the C4 and F8 electrodes are shown here. No sections ofdata have been rejected prior to calculating this PSD.In channel C4 α/µ band peaks are clearly observable inall of the cases. In addition, most clearly seen in test 3,after the first five minutes of recording there is also clearactivation of the motor cortex (C4) with a peak in frequencyactivity around 23 Hz. This peak is maintained throughoutthe rest of the experiment. The 23 Hz peak is also presentin test 2 from 5–20 minutes. In test case 1 small peaks inthis mid-β frequency range are present, but they are muchless pronounced.IV. DISCUSSIONIn Fig. 4, comparing channel C4 to F8, no peaks in the βband are seen in F8 or in the (not illustrated) other non-motor electrodes. This indicates that the mid-β peaks docorrespond to an activation of the motor cortex, as opposedto a more general activation of the brain, or the presenceof broad motion artefacts. Voluntary muscle movement haslong been associated with decreased β power (increasedβ desynchronisation) compared to a baseline resting case[15], [16]. No baseline resting case is illustrated in Fig. 4:instead Fig. 4 shows the evolution of the β band afterthe start of exercise. It suggests a potential decrease in βdesynchronisation as the exercise task increases in duration,with more activation in the C4 region several minutes afterthe start of exercise.At this point further insights into the origin of the 23 Hzpeaks, and why they only emerge after the first 5 minutesof the exercise task, are not available. Potentially there is anextended electrode-settling time, with this transitory propertyof the electrodes masking underlying low level signals at thestart of the experiment. (See [4] for a discussion of suchtransients in conventional EEG electrodes.) Alternativelya learning effect within the task, as the subject becomesfamiliar with the particular exercise bike used and settlesinto the process of riding it, cannot be ruled out. No periodof acclimatisation was incorporated into the experimentalprotocol.Nevertheless, the high time resolution neurophysiologicaldynamics of cycling—and moreover the temporal evolutionof the EEG and brain state—have not been well studiedpreviously. They can only be done so now due to theemergence of high quality dry electrode EEG approaches.As a result there are few comparison results available forvalidating the EEG trends identified here. [17] used PETscanning to monitor subjects while cycling and found asignificant activation of the primary motor cortex duringrhythmic cycling motion, consistent with the results here.To our knowledge, the only EEG based study is [16] whichused a reclining stationary bicycle allowing them to restrainthe user to minimise motion artefact and then use spongebased electrodes to record the EEG. Analysis in [16] focusedon the C1, Cz, C2 region and showed a pedalling relatedpotential at twice the pedalling frequency when the EEGfrom each cycle of the feet was averaged together. They alsocompared β band powers between rest and active movementand found no change in β desynchronisation. A significantdifference in desynchronisation was observed between trialswhere the subject pedalled and trails where the pedals weremoved for the subject, with just their feet resting on them.However, only the first 500 s (8 minutes) of EEG data wasused in this study, insufficient to investigate the temporalevolution of EEG powers as done here.The current study has thus demonstrated the potential util-ity of using dry EEG electrodes for high temporal resolutionbrain monitoring in non-conventional situations. This hasallowed the collection of preliminary results that suggest anincrease in mid-β activity during prolonged exercise.V. CONCLUSIONSDry EEG electrodes are now opening new avenues inwhere and when EEG monitoring can be performed. Thisstudy has used them to allow monitoring of the EEG during abike riding task and the results demonstrate that good qualityEEG signals can be collected which are not dominated byhigh-amplitude motion artefacts. Frequency domain analysisof these signals shows clear peaks in the α/µ band togetherwith a relative increase in mid-β activity occurring fiveminutes after the start of the cycling task. Inevitably further−10010 EEG test1PSD / dBμV/√HzEEG test1EEG test2 EEG test25 15 25 35EEG test3Frequency / Hz  5 15 25 35EEG test3Frequency / Hz  0 − 5 minutes  5 − 10 minutes10 − 15 minutes15 − 20 minutes−10010PSD / dBμV/√Hz−10010PSD / dBμV/√HzC4 F8Fig. 4. EEG Power Spectral Densities (PSD) found during the first 20 minutes of the bike riding task. This is calculated in non-overlapping five minuteepochs to show the PSD evolution over time. Left: EEG channel C4; Right: EEG channel F8. Highlighted is the region around 23 Hz in channel C4 whichshows a clear elevation in tests 2 and 3, with smaller peaks present in test 1. No such activations are present in the F8 channel.experimentation is necessary to validate these neurophysio-logical changes, but this paper has demonstrated that it isnow possible to use dry EEG recordings to enable EEGmonitoring in non-conventional situations. This opens newavenues for brain research using portable EEG monitors,and to moves electrode research beyond only comparing theperformance of wet and dry electrodes. As a result we arenow gaining initial insights into the on-going operation ofthe brain during exercise.VI. ACKNOWLEDGEMENTSThe authors would like to acknowledge and thank QuasarUSA (http://quasarusa.com/) for the loan of the DSI-miniEEG recorder used in this study.REFERENCES[1] H. Berger, “Uber das eletrenkephalogram des menschen,” Arch. Psy-chiatr. Nervenkr., vol. 87, no. 1, pp. 527–570, 1929.[2] A. J. Casson, D. C. Yates, S. J. Smith, J. S. Duncan, and E. Rodriguez-Villegas, “Wearable electroencephalography,” IEEE Eng. Med. Biol.Mag., vol. 29, no. 3, pp. 44–56, 2010.[3] E. Huigen, A. Peper, and C. A. Grimbergen, “Investigation into theorigin of the noise of surface electrodes,” Med. Biol. Eng. Comput.,vol. 40, no. 3, pp. 332–338, 2002.[4] E. Camacho-Galeano, C. Galup-Montoro, and M. Schneider, “Evalu-ation of commercially available electrodes and gels for recording ofslow EEG potentials,” Clin. Neurophysiol., vol. 116, no. 4, pp. 799–806, 2005.[5] Y. Chi, T.-P. Jung, and G. Cauwenberghs, “Dry-contact and noncontactbiopotential electrodes: Methodological review,” IEEE Rev. Biomed.Eng., vol. 3, no. 1, pp. 106–119, 2010.[6] A. J. Casson, “Towards noise-enhanced augmented cognition,” in HCIinternational, Las Vegas, July 2013.[7] J. Xu, R. F. Yazicioglu, B. Grundlehner, P. Harpe, K. A. A. Makinwa,and C. Van Hoof, “A 160 µW 8-channel active electrode system forEEG monitoring,” IEEE Trans. Biomed. Circuits Syst., vol. 5, no. 6,pp. 555–567, 2011.[8] R. Matthews, N. J. McDonald, P. Hervieux, P. J. Turner, and M. A.Steindorf, “A wearable physiological sensor suite for unobtrusivemonitoring of physiological and cognitive state,” in IEEE EMBC,Lyon, August 2007.[9] G. Gargiulo, P. Bifulco, R. A. Calvo, M. Cesarelli, C. Jin, and A. vanSchaik, “A mobile EEG system with dry electrodes,” in IEEE BioCAS,Baltimore, November 2008.[10] IMEC. (2012) Holst centre and panasonic present wireless low-power active-electrode EEG headset. [Online]. Available: http://www.imec.be/[11] S. Patki, B. Grundlehner, A. Verwegen, S. Mitra, J. Xu, A. Matsumoto,R. F. Yazicioglu, and J. Penders, “Wireless EEG system with realtime impedance monitoring and active electrodes,” in IEEE BioCAS,Hsinchu, November 2012.[12] J. R. Estepp, J. C. Christensen, J. W. Monnin, I. M. Davis, and G. F.Wilson, “Validation of a dry electrode system for EEG,” in Proc.HFES, San Antonio, October 2009.[13] W. O. Tatum, B. A. Dworetzky, and D. L. Schomer, “Artifact andrecording concepts in EEG,” J. Clin. Neurophysiol., vol. 28, no. 3, pp.252–263, 2011.[14] J. W. Britton, “Electroencephalographic artifacts and benign variants,”in Aminoff’s Electrodiagnosis in Clinical Neurology, 6th ed., M. J.Aminoff, Ed. Amsterdam: Elsevier Saunders, 2012, pp. 129–142.[15] G. E. Chatrian, M. C. Petersen, and A. Lazarte, J, “The blockingof the rolandic wicket rhythm and some central changes related tomovement,” Electroencephalogr. and Clin. Neurophysiol., vol. 11,no. 3, pp. 497–510, 1959.[16] S. Jain, K. Gourab, S. Schindler-Ivens, and B. D. Schmit, “EEGduring pedaling: Evidence for cortical control of locomotor tasks,”Clin. Neurophysiol., vol. 124, no. 2, pp. 379–390, 2013.[17] L. Christensen, P. Johannsen, T. Sinkjaer, N. Petersen, H. Pyndt, andJ. Nielsen, “Cerebral activation during bicycle movements in man,”Exp. Brain Res., vol. 135, no. 1, pp. 66–72, 2000.

Towards out-of-the-lab EEG in uncontrolled environments: feasibility study of dry EEG recordings during exercise bike riding

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Towards out-of-the-lab EEG in uncontrolled environments: feasibility study of dry EEG recordings during exercise bike riding

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