Journal of Neuroscience Methods 235 (2014) 298–307
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A direct comparison of active and passive ampliﬁcation electrodes in the same ampliﬁer system Sarah Laszlo a,b,∗ , Maria Ruiz-Blondet c , Negin Khaliﬁan a , Fanny Chu d , Zhanpeng Jin c,e a
Department of Psychology, SUNY Binghamton, United States Program in Linguistics, SUNY Binghamton, United States Department of Bioengineering, SUNY Binghamton, United States d Department of Criminal Justice, Michigan State University, United States e Department of Electrical and Computer Engineering, SUNY Binghamton, United States b c
h i g h l i g h t s • • • •
We directly tested, for the ﬁrst time, whether active ampliﬁcation electrodes produce higher quality data than passive ones. We determined that in ideal conditions, traditional, passive ampliﬁcation electrodes still produce the absolute best data. However, we also found that at higher levels of interelectrode impedance, active electrodes always outperform passive ones. We found that interelectrode impedance effects interact with voltage stability in active ampliﬁcation electrodes only.
a r t i c l e
i n f o
Article history: Received 5 March 2014 Received in revised form 10 May 2014 Accepted 12 May 2014 Available online 27 July 2014 Keywords: Electrophysiological methods Active ampliﬁcation Interelectrode impedance
a b s t r a c t Background: Active ampliﬁcation electrodes are becoming more popular for ERP data collection, as they amplify the EEG at the scalp and thereby potentially decrease the inﬂuence of ambient electrical noise. However, the performance of active electrodes has not been directly compared with that of passive electrodes in the context of collecting ERPs from a cognitive task. Here, the performance of active and passive ampliﬁcation electrodes in the same digitizing ampliﬁer system was examined. Method: In Experiment 1, interelectrode impedance in an electrically quiet setting was manipulated to determine whether, in such recording conditions, active electrodes can outperform passive ones. In Experiment 2, the performance of active electrodes at the limits of natural skin impedance was explored, as was the relationship between active ampliﬁcation circuitry and voltage stability in averaged EOG. Results: Results reveal a complex pattern of interrelations between electrode type, impedance, and voltage stability, indicating that which type of electrode is “best” depends non-trivially on the circumstances in which data are being collected. Comparison with existing methods: Traditional, passive electrodes acquired the cleanest data observed in any of the acquisition conditions at very low impedance, but not at any impedance >2 k. Conclusion: Active electrodes perform better than passive ones at all impedances other than very low ones; however, this is qualiﬁed by the additional ﬁnding that during fast voltage ﬂuctuations, such as those most desirable in most ERP studies, active electrodes are less able to accurately follow the EEG than passive ones. © 2014 Elsevier B.V. All rights reserved.
1. Introduction A substantial source of undesired noise in EEG recordings is produced by the intersection of electrode leads and ambient
∗ Corresponding author at: Department of Psychology, 4400 Vestal Parkway East, Binghamton, NY 13902, United States. Tel.: +1 607 777 3380. E-mail addresses: [email protected]
, [email protected]
(S. Laszlo). http://dx.doi.org/10.1016/j.jneumeth.2014.05.012 0165-0270/© 2014 Elsevier B.V. All rights reserved.
electrical activity (e.g., electrical noise created by nearby computers). Essentially, the electrode lead acts as an antenna, picking up ambient electrical activity, such as that from a stimulus presentation computer monitor, and transmitting it along with the EEG signal of interest to the digitizing ampliﬁer (see Metting Van Rijn et al., 1990). The ambient electrical activity (typically observed as line noise in North America at 60 Hz and in Europe at 50 Hz, to give two examples) then contaminates the biological signal of interest. One solution to this problem is to amplify the EEG directly at
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the electrode, prior to entering the electrode lead, thereby obviating the problem of noise entering the signal prior to ampliﬁcation (see Metting Van Rijn et al., 1996). This strategy is called active ampliﬁcation. Because active ampliﬁcation electrodes include additional circuitry not needed by traditional passive ampliﬁcation electrodes, they are currently approximately twice as expensive as passive electrodes: Passive ampliﬁcation electrodes cost about $40 each, and active ampliﬁcation electrodes cost around $80 each. This expense is potentially justiﬁed by two considerations. The ﬁrst, as described above, is that active ampliﬁcation may reduce ambient electrical noise in the digitized EEG. Even in the lab, EEG is typically digitized in the presence of computers and computer monitors, which can be sources of undesirable electrical noise. In applied or therapeutic settings, less control is available over the electrical properties of the environment, making ambient noise an even larger problem. The potential to record clean data in electrically compromised environments is a primary draw of active electrodes. A second potential justiﬁcation for the higher cost of active electrodes is that they are claimed to provide a cleaner signal at higher interelectrode impedance than passive electrodes (e.g., BioSemi, 2013; Brain Products, 2009) because they are “immune”, up to a certain degree, to the interelectrode impedance, a primary factor in the amplitude of noise in the EEG (see below). Recall that interelectrode impedance is the ease with which electrical current passes between electrodes through the tissue of the scalp (for review, see Kappenman and Luck, 2010), and that traditional best practices in acquiring the EEG suggest that interelectrode impedance be reduced to less than 10 k (Keil et al., 2014). High impedance data recording, however, is desirable because it requires less preparation time than low impedance recording—especially when many electrodes are used. In order to lower interelectrode impedance below 10 k, it is typically necessary to gently abrade the skin beneath each electrode, in order to remove oils and dead skin cells that resist the ﬂow of current between electrodes (see Picton and Hillyard, 1972). This procedure becomes more and more time consuming as more electrodes are used in recording. Further, it can be aversive, especially to populations like children and infants; at the very least it is inconvenient, making low impedance recording unrealistic for applied or consumer settings. Even in the lab, in individuals who do not ﬁnd the procedure aversive, the fact that the skin is abraded presents a possible vector for the transmission of infection and disease, which is clearly undesirable (see Putnam et al., 1992). For each of these reasons, being able to collect satisfactory data at a relatively high level of interelectrode impedance is appealing. Here, “high” impedance will be considered to be any value above the best-practices level of 10 k—in the experiments described below “high” impedance will range between 35 and 50 k. Active electrodes are often billed as enabling this mode of data collection, and this feature is taken as an additional justiﬁcation of their relatively high cost. The follow example illustrates why active electrodes should have this property. In passive ampliﬁcation systems, interference currents that come from the main power and “wirelessly” couple to the participant and to the electrode wires (capacitive coupling), multiply by the interelectrode impedance that gives as a result the interference voltage that corrupts the EEG signal before it gets to the ampliﬁer (Metting Van Rijn et al., 1990). A typical interference current is of the order of 20 nA, which, given an interelectrode impedance of even 10 k, yields an interference voltage of 200 V, a magnitude capable of drowning the EEG signal being measured, which is normally between 10 and 100 V (Aurlien et al., 2004). Active electrodes, in contrast, have the capability of acquiring the voltage from the source (as opposed to at the ampliﬁer) at high input impedance, avoiding current absorption. This is desirable, because current absorption decreases the voltage being measured
(Metting Van Rijn et al., 1990). An additional important characteristic is that active ampliﬁcation electrodes implement an ampliﬁer that offers low output impedance independently from the magnitude of the interelectrode impedance (Metting Van Rijn et al., 1990). That is, regardless of the impedance between electrodes, the impedance to the signal output from the ampliﬁer remains low. Then, with regard to the previous example, if there is an interference current of 20 nA, given a traditional ampliﬁer output impedance of 2 k, active electrodes recorded data that was as good as or better than that collected by passive ones. This result relates to the answer to the second question: are active electrodes, as claimed (by manufacturers) and as perceived (seemingly by researchers) actually less sensitive to interelectrode impedance than passive electrodes? The answer to this question is yes, under certain conditions. In the baseline, when voltage is stable, it does seem to be the case that active electrodes display a negligible impedance effect, up to and including at the impedance of unprepared skin, while passive electrodes
S. Laszlo et al. / Journal of Neuroscience Methods 235 (2014) 298–307
display a large impedance effect. However, during rapid voltage ﬂuctuations—that is, exactly the kind of data that cognitive ERP experiments are most interested in— active electrodes’ imperviousness to impedance disappears, instead displaying a reliable impedance effect, just like passive electrodes. It is this relatively complex relationship between impedance and voltage change that allows for the claims of active electrode manufacturers to be “true” in that active electrodes are impervious to interelectrode impedance when voltage is stable, but not “completely true” in that active electrodes are not impervious to interelectrode impedance when voltage is changing rapidly, which, crucially, is the situation that most cognitive ERP researchers actually care about. One extremely pertinent question left open by the discussion thus far is the degree to which these results would generalize to active electrode systems besides those examined here. While Ag/AgCl is fairly similar across manufacturers (and pure tin or pure gold—two other materials frequently used in passive electrodes even more so), the exact analog circuitry employed in active ampliﬁcation electrodes from different manufacturers may vary. Formal circuit analysis can make us conﬁdent in at least the following: any semiconductor device, such as the ampliﬁer within the active electrode, has a ﬁnite limit in its slew rate, which can result in output signal distortion in portions of the input signal where there is a high rate of change of voltage. Finally, it is important to emphasize, as has been done by others conducting similar research (Kappenman and Luck, 2010), that the results obtained here are not absolute. As just discussed, only active electrodes with a relatively low slew rate that suffer from nonlinear distortion are guaranteed to behave in this manner. More broadly, other labs are likely to differ in numerous characteristics that can impact EEG and EOG recording and digitization: the presence or absence of sound-proofed, RF-shielded chambers; the ambient temperature and humidity, the electrical and auditory environment in which data are collected. As discussed, it is always important to validate the properties of any data acquisition system in one’s own lab and for one’s own applications.
Acknowledgments The authors acknowledge K. D. Federmeier, G. A, Miller, B. C. Armstrong, and E. W. Wlotko for their insightful contributions to this manuscript, and A. Bennett, A. Francino, H. Faigen, C. Greenwald, E. Heller, K. Mooney, & P. Melman for their assistance with data collection. This research was supported by the Research Foundation of the State University of New York (#118394) and NSFCAREER BCS-1252975 (S.L.), and the Binghamton University Health Sciences Transdisciplinary Area of Excellence (#114993) (S.L. and Z.J.). References Aurlien H, Gjerde IO, Aarseth JH, Eldøen G, Karlsen B, Skeidsvoll H, et al. EEG background activity described by a large computerized database. Clin Neurophysiol 2004;115(3):665–73. BioSemi Co. Is skin preparation necessary when using BioSemi active electrodes? In BioSemi Frequently Asked Questions; n.d. Retrieved April 15, 2013 from http://www.biosemi.com/faq/skin preparation.htm Brain Products GmbH. BCI–biofeedback: brain computer interface and brain machine interface. [brochure]. Munich, Germany; 2009. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. J Neurosci Methods 2004;134:9–21. Kappenman ES, Luck SJ. The effects of electrode impedance on data quality and statistical signiﬁcance in ERP recordings. Psychophysiology 2010;47:888–904. Keil A, Debener S, Gratton G, Junghöfer M, Kappenman ES, Luck SJ, et al. Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. Psychophysiology 2014;51:1–21. Metting Van Rijn AC, Peper A, Grimbergen CA. High-quality recording of bioelectric events I: interference reduction, theory, and practice. Med Biol Eng Comput 1990;28:389–97. Metting Van Rijn AC, Kuiper AP, Dankers TE, Grimbergen CA. Low-cost active electrode improves the resolution in biopotential recordings. Engineering in Medicine and Biology Society. Bridging Disciplines for Biomedicine Proceedings of the 18th Annual International Conference of the IEEE, October, vol. 1. IEEE; 1996. p. 101–2. Nonclercq A, Mathys P. Quantiﬁcation of motion artifact rejection due to active electrodes and driven-right-leg circuit in spike detection algorithms. IEEE Trans Biomed Eng 2010;57(11):2746–52. Picton TW, Hillyard SA. Cephalic skin potentials in electroencephalography. Electroencephalogr Clin Neurophysiol 1972;33:419–24. Putnam LE, Johnson R, Roth WT. Guidelines for reducing the risk of disease transmission in the psychophysiology laboratory. Psychophysiology 1992;29(2):127–41.