Reference Electrode Standardization Technique Research Articles

Selecting methods for a modular EEG pre-processing pipeline: An objective comparison

Electroencephalography (EEG) to study brain functions has become fundamental in many research settings across very different protocols. Indeed, a plethora of processing methods have been developed, for both data preparation (pre-processing) and analysis. While an effect of the pre-processing on the signal is admitted and accepted, there is an increasing effort to better understand to which extent such an influence may affect the analysis results, and which may be the best practices for the correct data pre-processing.Pre-processing procedures include different steps, and each of them may induce modifications affecting the study results. Thus, we analyze the effect of different methodologies at each step and propose quantitative parameters for the choice of the preferable strategy.We illustrate how method selection may affect the quality of EEG signal in an Action Observation and Motor Imagery protocol, using quantitative indices. We analyzed the effect of different strategies for early-stage data preparation; two independent component analysis (ICA) algorithms (SOBI and Extended Infomax) used for artifact removal; and four re-reference approaches (Common averaged reference-CAR, robust-CAR, reference electrode standardization technique – REST, and reference electrode standardization and interpolation technique – RESIT). The effects of the different pipelines were also evaluated through the computation of event-related spectral perturbation (ERSP) of the sensorimotor rhythm. Results showed that signal segmentation significantly affects the cleaning procedure, while comparable results are obtained across ICA approaches. Finally, similar topographical representations were obtained after the application of CAR, REST, and RESIT re-referencing approaches, where rCAR showed the most different ERSP topographical pattern.

Rereferencing of clinical EEGs with nonunipolar mastoid reference to infinity reference by REST

ObjectiveConventional electroencephalography (EEG) offline subtraction rereferencing is invalid for many clinical practices when adopting a specific nonunipolar recording montage (e.g., the ipsilateral mastoid (IM) and contralateral mastoid (CM)). Further comparative analyses would thus be blocked due to the lack of a uniform offline reference. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform nonunipolar mastoid montages into a computational zero reference at infinity (IR) offline. MethodsFor EEG signals and power/connectivity configurations, simulation and clinical schizophrenia resting-state EEG datasets were used to investigate the performance of REST. ResultsREST produced small absolute errors (signal level: 1.21–1.26; power: 0.0057–0.021; connectivity: 0.066–0.088) and high correlations (>0.9) between the IM/CM-IR and true IR references. Using clinical data with the IM online reference, REST revealed valuable changes in spectral and connectivity (P < 0.05) in schizophrenia patients, consistent with previous studies. ConclusionsThese results demonstrated that REST transformation could be adopted to resolve the offline rereferencing of clinical EEGs with specific nonunipolar mastoid references. SignificanceREST could be an effective and robust resolution for nonunipolar clinical EEGs and could therefore retrieve these data for further analysis by deriving a favorable offline reference IR.

Selecting a pre-processing pipeline for the analysis of EEG event-related rhythms modulation.

When deciding how to pre-process EEG data, researchers need to make a choice at each single step of the procedure among different possibilities, equally valid. Therefore, in this work, we illustrate how these decisions may affect the quality of the final cleaned data in an Action Observation/Motor Imagery protocol, using quantitative indices. In particular, we showed the effect of segmenting or not the data in epochs around the stimulus presentation time on the independent component analysis (ICA) used for artifact removal. For ICA analysis, we tested two algorithms (SOBI and Extended Infomax). Finally, three re-reference approaches (Common averaged reference-CAR, robust-CAR and reference electrode standardization technique - REST) were also applied and their effects compared. Results showed that the segmenting method has a prominent effect on the cleaning procedure and consequently on final EEG data quality. Extended Infomax is confirmed as the method of choice for the identification of the artifactual components and, finally, CAR and the REST re-referencing techniques led to similar good results.

The Effects of Different Reference Methods on Decision-Making Implications of Auditory Brainstem Response.

Hearing loss is a common disease affecting public health all around the world. In clinic, auditory brainstem response (ABR) has been widely used for the detection of hearing loss based on its convenience and accuracy. The different reference methods directly influence the quality of the ABR waveform which in turn affects the ABR-based diagnosis. Therefore, in this study, a reference electrode standardization technique (REST) was adopted to systematically investigate and evaluate the effect of different reference methods on the quality of ABR waveform in comparison with the conventional average reference (AR) and mean mastoid (MM) methods. In this study, ABR signals induced by click stimulus were acquired via an EEG electrode cap arrays, and those located on the six channels along the midline were compared systemically. The results showed that, when considering the different channels, the ABR in the Cz channel showed the best morphology. Then, the ABR waveforms acquired via the REST method possessed better morphologies with large amplitude (0.06 ± 0.02 μV for wave I, 0.07 ± 0.02 μV for wave III, and 0.21 ± 0.04 μV for wave V) when compared with the traditional method. Summarily, we found that the REST and MM methods improved the quality of ABR on both amplitude and morphology under different stimulation rates and levels without changing the latencies of ABR when compared with the conventional AR method, suggesting that the REST and MM methods have the potential to help physicians with high accurate ABR-based clinical diagnosis. Moreover, this study might also provide a theoretic basis of reference methods on the acquisition of electroencephalogram over public health issues.

The role of electroencephalography electrical reference in the assessment of functional brain–heart interplay: From methodology to user guidelines

BackgroundThe choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet. MethodsThis study identifies the best methodology combining a proper EEG electrical reference and signal processing methods for an effective functional BHI assessment. The effects of the EEG reference among common average, mastoids average, Laplacian reference, Cz reference, and the reference electrode standardization technique (REST) were explored throughout different BHI methods including synthetic data generation (SDG) model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient. ResultsThe SDG model exhibited high robustness between EEG references, whereas the maximal information coefficient method exhibited a high sensitivity. The common average and REST references for EEG showed a good consistency in the between-method comparisons. Laplacian, and Cz references significantly bias a BHI measurement. Comparison with existing methodsThe use of EEG reference based on a common average outperforms on the use of other references for consistency in estimating directed functional BHI. We do not recommend the use of EEG references based on analytical derivations as the experimental conditions may not meet the requirements of their optimal estimation, particularly in clinical settings. ConclusionThe use of a common average for EEG electrical reference is concluded to be the most appropriate choice for a quantitative, functional BHI assessment.

The Influence of Different EEG References on Scalp EEG Functional Network Analysis During Hand Movement Tasks

Although scalp EEG functional networks have been applied to the study of motor tasks using electroencephalography (EEG), the selection of a suitable reference electrode has not been sufficiently researched. To investigate the effects of the original reference (REF-CZ), the common average reference (CAR), and the reference electrode standardization technique (REST) on scalp EEG functional network analysis during hand movement tasks, EEGs of 17 right-handed subjects performing self-paced hand movements were collected, and scalp functional networks [coherence (COH), phase-locking value (PLV), phase lag index (PLI)] with different references were constructed. Compared with the REF-CZ reference, the networks with CAR and REST references exhibited more significant increases in connectivity during the left-/right-hand movement preparation (MP) and movement execution (ME) stages. The node degree of the channel near the reference electrode was significantly reduced by the REF-CZ reference. CAR and REST both decreased this reference effect, REST more so than CAR. We confirmed that the choice of reference would affect the analysis of the functional network during hand movement tasks, and the REST reference can greatly reduce the effects of the online recording reference on the analysis of EEG connectivity.

A Comparative Study of Different EEG Reference Choices for Event-Related Potentials Extracted by Independent Component Analysis.

In the event-related potential (ERP) of scalp electroencephalography (EEG) studies, the vertex reference (Cz), linked mastoids or ears (LM), and average reference (AVG) are popular reference methods, and the reference electrode standardization technique (REST) is increasingly applied. Because scalp EEG recordings are considered as spatially degraded signals, independent component analysis (ICA) is a widely used data-driven method for obtaining ERPs by decomposing EEG data. However, the accurate estimation of the differences in ERP components extracted by ICA with different references remains unclear. In this study, we first provided formal descriptions of the above reference methods (Cz, LM, AVG, and REST) and ICA decomposition in ERP and then investigated the influences of different reference techniques on simulation and real EEG datasets. The results revealed that (1) the reference method did not change the peak amplitudes and latencies of relative ERPs corresponding to some IC time courses; (2) there were non-negligible effects of different reference methods on both temporal ERPs and spatial topographies of some ICs; and (3) compared to Cz, LM, and AR, considering both the performances of temporal ERPs and spatial topographies, the REST reference had overall superiority. These findings provide a recommended choice of REST for ICA analysis at the trial level and contribute to empirical investigations regarding the use of reference methods in ERP domains with ICA analysis.

Influence of EEG References on N170 Component in Human Facial Recognition.

The choice of the reference electrode scheme is an important step in event-related potential (ERP) analysis. In order to explore the optimal electroencephalogram reference electrode scheme for the ERP signal related to facial recognition, we investigated the influence of average reference (AR), mean mastoid reference (MM), and Reference Electrode Standardization Technique (REST) on the N170 component via statistical analysis, statistical parametric scalp mappings (SPSM) and source analysis. The statistical results showed that the choice of reference electrode scheme has little effect on N170 latency (p > 0.05), but has an significant impact on N170 amplitude (p < 0.05). ANOVA results show that, for the three references scheme, there was statistically significant difference between N170 latency and amplitude induced by the unfamiliar face and that induced by the scrambled face (p < 0.05). Specifically, the SPSM results show an anterior and a temporo-occipital distribution for AR and REST, whereas just anterior distribution shown for MM. However, the circumstantial evidence provided by source analysis is more consistent with SPSM of AR and REST, compared with that of MM. These results indicate that the experimental results under the AR and REST references are more objective and appropriate. Thus, it is more appropriate to use AR and REST reference scheme settings in future facial recognition experiments.

Characteristic changes in the EEG signals between microsleeps and preceding responsive states.

This work aims at identifying characteristic features of EEG to demarcate a microsleep from preceding responsive states. The EEG signals, after reference electrode standardization technique (REST) re-referencing, were processed through a time-varying general linear Kalman filter (TVGLKF) to derive time-varying auto-regressive (TVAR) parameters. The time-varying effective connectivity measure of orthogonal partial directed coherence (OPDC) was obtained for every instant at 256 Hz. Effective connectivity matrices formed using these OPDC measures, with the scalp electrodes as nodes were processed further using graph theory. Community-based measures were investigated and statistical significances compared. Non-parametric Wilcoxon signed rank test was used for significance analysis, with Cohen-type and Common Language effect size (CLES) as measures of effect sizes. The results showed a decrease in directional modularity from anterior to posterior, in theta, alpha, and beta bands in microsleeps. The alpha band showed the highest significance with a Cohen-type effect size of 1.25 and a median percentage difference of 23% across subjects, with a range of 13-28%. Flexibility and integration also decreased with average percentage of 25% (17-35%) and 20% (16-32%), respectively, while recruitment increased on an average of 11% (3-16%), wherever significant across all bands. These community-based measures can help characterize and explain changes in brain mechanisms, and can also serve as potential biomarkers for microsleep detection.

The Statistics of EEG\xa0Unipolar References: Derivations and Properties

In this brief communication, which complements the EEG reference review (Yao et al. in Brain Topogr, 2019), we provide the mathematical derivations that show: (1) any EEG reference admits the general form of a linear transformation of the ideal multichannel EEG potentials with reference to infinity; (2) the average reference (AR), the reference electrode standardization technique (REST), and its regularized version (rREST) are solving the linear inverse problems that can be derived from both the maximum likelihood estimate (MLE) and the Bayesian theory; however, REST is based on more informative prior/constraint of volume conduction than that of AR; (3) we show for the first time that REST is also a unipolar reference (UR), allowing us to define a general family of URs with unified notations; (4) some notable properties of URs are ‘no memory’, ‘rank deficient by 1’, and ‘orthogonal projector centering’; (5) we also point out here, for the first time, that rREST provides the optimal interpolating function that can be used when the reference channel is missing or the ‘bad’ channels are rejected. The derivations and properties imply that: (a) any two URs can transform to each other and referencing with URs multiple times will not accumulate artifacts; (b) whatever URs the EEG data was previously transformed with, the minimum norm solution to the reference problem will be REST and AR with and without modeling volume conduction, respectively; (c) the MLE and the Bayesian theory show the theoretical optimality of REST. The advantages and limitations of AR and REST are discussed to guide readers for their proper use.

Electrophysiological signatures of the resting-state fMRI global signal: A simultaneous EEG-fMRI study

BackgroundThe global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined. New methodsWe developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects. ResultsWe found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the γ band (30–45 Hz) and a negative correlation in the low-frequency band (4–20 Hz). Comparison with existing method(s)Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity. ConclusionsThese results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.

Sensor Level Functional Connectivity Topography Comparison Between Different References Based EEG and MEG.

Sensor-level functional connectivity topography (sFCT) contributes significantly to our understanding of brain networks. sFCT can be constructed using either electroencephalography (EEG) or magnetoencephalography (MEG). Here, we compared sFCT within the EEG modality and between EEG and MEG modalities. We first used simulations to look at how different EEG references—including the Reference Electrode Standardization Technique (REST), average reference (AR), linked mastoids (LM), and left mastoid references (LR)—affect EEG-based sFCT. The results showed that REST decreased the reference effects on scalp EEG recordings, making REST-based sFCT closer to the ground truth (sFCT based on ideal recordings). For the inter-modality simulation comparisons, we compared each type of EEG-sFCT with MEG-sFCT using three metrics to quantize the differences: Relative Error (RE), Overlap Rate (OR), and Hamming Distance (HD). When two sFCTs are similar, RE and HD are low, while OR is high. Results showed that among all reference schemes, EEG-and MEG-sFCT were most similar when the EEG was REST-based and the EEG and MEG were recorded simultaneously. Next, we analyzed simultaneously recorded MEG and EEG data from publicly available face-recognition experiments using a similar procedure as in the simulations. The results showed (1) if MEG-sFCT is the standard, REST—and LM-based sFCT provided results closer to this standard in the terms of HD; (2) REST-based sFCT and MEG-sFCT had the highest similarity in terms of RE; (3) REST-based sFCT had the most overlapping edges with MEG-sFCT in terms of OR. This study thus provides new insights into the effect of different reference schemes on sFCT and the similarity between MEG and EEG in terms of sFCT.

Unified Bayesian Estimator of EEG Reference at Infinity: rREST (Regularized Reference Electrode Standardization Technique).

The choice of reference for the electroencephalogram (EEG) is a long-lasting unsolved issue resulting in inconsistent usages and endless debates. Currently, both the average reference (AR) and the reference electrode standardization technique (REST) are two primary, apparently irreconcilable contenders. We propose a theoretical framework to resolve this reference issue by formulating both (a) estimation of potentials at infinity, and (b) determination of the reference, as a unified Bayesian linear inverse problem, which can be solved by maximum a posterior estimation. We find that AR and REST are very particular cases of this unified framework: AR results from biophysically non-informative prior; while REST utilizes the prior based on the EEG generative model. To allow for simultaneous denoising and reference estimation, we develop the regularized versions of AR and REST, named rAR and rREST, respectively. Both depend on a regularization parameter that is the noise to signal variance ratio. Traditional and new estimators are evaluated with this framework, by both simulations and analysis of real resting EEGs. Toward this end, we leverage the MRI and EEG data from 89 subjects which participated in the Cuban Human Brain Mapping Project. Generated artificial EEGs—with a known ground truth, show that relative error in estimating the EEG potentials at infinity is lowest for rREST. It also reveals that realistic volume conductor models improve the performances of REST and rREST. Importantly, for practical applications, it is shown that an average lead field gives the results comparable to the individual lead field. Finally, it is shown that the selection of the regularization parameter with Generalized Cross-Validation (GCV) is close to the “oracle” choice based on the ground truth. When evaluated with the real 89 resting state EEGs, rREST consistently yields the lowest GCV. This study provides a novel perspective to the EEG reference problem by means of a unified inverse solution framework. It may allow additional principled theoretical formulations and numerical evaluation of performance.

The Scalp Time-Varying Networks of N170: Reference, Latency, and Information Flow.

Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP) appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG) references, namely, AR (average of all recording channels) and reference electrode standardization technique (REST), were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events.

Electrophysiological Responses to Expectancy Violations in Semantic and Gambling Tasks: A Comparison of Different EEG Reference Approaches.

Dynamically evaluating the outcomes of our actions and thoughts is a fundamental cognitive ability. Given its excellent temporal resolution, the event-related potential (ERP) technology has been used to address this issue. The feedback-related negativity (FRN) component of ERPs has been studied intensively with the averaged linked mastoid reference method (LM). However, it is unknown whether FRN can be induced by an expectancy violation in an antonym relations context and whether LM is the most suitable reference approach. To address these issues, the current research directly compared the ERP components induced by expectancy violations in antonym expectation and gambling tasks with a within-subjects design and investigated the effect of the reference approach on the experimental effects. Specifically, we systematically compared the influence of the LM, reference electrode standardization technique (REST) and average reference (AVE) approaches on the amplitude, scalp distribution and magnitude of ERP effects as a function of expectancy violation type. The expectancy deviation in the antonym expectation task elicited an N400 effect that differed from the FRN effect induced in the gambling task; this difference was confirmed by all the three reference methods. Both the amplitudes of the ERP effects (N400 and FRN) and the magnitude as the expectancy violation increased were greater under the LM approach than those under the REST approach, followed by those under the AVE approach. Based on the statistical results, the electrode sites that showed the N400 and FRN effects critically depended on the reference method, and the results of the REST analysis were consistent with previous ERP studies. Combined with evidence from simulation studies, we suggest that REST is an optional reference method to be used in future ERP data analysis.

Hearing the Sound in the Brain: Influences of Different EEG References.

If the scalp potential signals, the electroencephalogram (EEG), are due to neural “singers” in the brain, how could we listen to them with less distortion? One crucial point is that the data recording on the scalp should be faithful and accurate, thus the choice of reference electrode is a vital factor determining the faithfulness of the data. In this study, music on the scalp derived from data in the brain using three different reference electrodes were compared, including approximate zero reference—reference electrode standardization technique (REST), average reference (AR), and linked mastoids reference (LM). The classic music pieces in waveform format were used as simulated sources inside a head model, and they were forward calculated to scalp as standard potential recordings, i.e., waveform format music from the brain with true zero reference. Then these scalp music was re-referenced into REST, AR, and LM based data, and compared with the original forward data (true zero reference). For real data, the EEG recorded in an orthodontic pain control experiment were utilized for music generation with the three references, and the scale free index (SFI) of these music pieces were compared. The results showed that in the simulation for only one source, different references do not change the music/waveform; for two sources or more, REST provide the most faithful music/waveform to the original ones inside the brain, and the distortions caused by AR and LM were spatial locations of both source and scalp electrode dependent. The brainwave music from the real EEG data showed that REST and AR make the differences of SFI between two states more recognized and found the frontal is the main region that producing the music. In conclusion, REST can reconstruct the true signals approximately, and it can be used to help to listen to the true voice of the neural singers in the brain.

Event-Related Potential Responses to Task Switching Are Sensitive to Choice of Spatial Filter.

Event-related potential (ERP) studies using the task-switching paradigm show that multiple ERP components are modulated by activation of proactive control processes involved in preparing to repeat or switch task and reactive control processes involved in implementation of the current or new task. Our understanding of the functional significance of these ERP components has been hampered by variability in their robustness, as well as their temporal and scalp distribution across studies. The aim of this study is to examine the effect of choice of reference electrode or spatial filter on the number, timing and scalp distribution of ERP elicited during task-switching. We compared four configurations, including the two most common (i.e., average mastoid reference and common average reference) and two novel ones that aim to reduce volume conduction (i.e., reference electrode standardization technique (REST) and surface Laplacian) on mixing cost and switch cost effects in cue-locked and target-locked ERP waveforms in 201 healthy participants. All four spatial filters showed the same well-characterized ERP components that are typically seen in task-switching paradigms: the cue-locked switch positivity and target-locked N2/P3 effect. However, both the number of ERP effects associated with mixing and switch cost, and their temporal and spatial resolution were greater with the surface Laplacian transformation which revealed rapid temporal adjustments that were not identifiable with other spatial filters. We conclude that the surface Laplacian transformation may be more suited to characterize EEG signatures of complex spatiotemporal networks involved in cognitive control.

How do reference montage and electrodes setup affect the measured scalp EEG potentials?

Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. Approach. First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. Main results. Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. Significance. These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for cognitive neuroscientists and clinicians.

How Electroencephalogram Reference Influences the Movement Readiness Potential?

Readiness potential (RP) based on electroencephalograms (EEG) has been studied extensively in recent years, but no studies have investigated the influence of the reference electrode on RP. In order to investigate the reference effect, 10 subjects were recruited and the original vertex reference (Cz) was used to record the raw EEG signal when the subjects performed a motor preparation task. The EEG was then transformed to the common average reference (CAR) and reference electrode standardization technique (REST) reference, and we analyzed the RP waveform and voltage topographies and calculated the classification accuracy of idle and RP EEG segments. Our results showed that the RP waveform and voltage topographies were greatly influenced by the reference, but the classification accuracy was less affected if proper channels were selected as features. Since the Cz channel is near the primary motor cortex, where the source of RP is located, using the REST and CAR references is recommended to get accurate RP waveforms and voltage topographies.

Effect of Different References on Auditory-Evoked Potentials in Children with Cochlear Implants.

Background: Nose reference (NR), mastoid reference (MR), and montage average reference (MAR) are usually used in auditory event-related potential (AEP) studies with a recently developed reference electrode standardization technique (REST), which may reduce the reference effect. For children with cochlear implants (CIs), auditory deprivation may hinder normal development of the auditory cortex, and the reference effect may be different between CIs and a normal developing group.Methods: Thirteen right-side-CI children were recruited, comprising 7 males and 6 females, ages 2–5 years, with CI usage of ~1 year. Eleven sex- and age-matched healthy children were recruited for normal controls; 1,000 Hz pure tone evoked AEPs were recorded, and the data were re-referenced to NR, left mastoid reference (LMR, which is the opposite side of the implanted cochlear), MAR, and REST. CI artifact and P1–N1 complex (latency, amplitudes) at Fz were analyzed.Results: Confirmed P1–N1 complex could be found in Fz using NR, LMR, MAR, and REST with a 128-electrode scalp. P1 amplitude was larger using LMR than MAR and NR, while no statistically significant difference was found between NR and MAR in the CI group; REST had no significant difference with the three other references. In the control group, no statistically significant difference was found with different references. Group difference of P1 amplitude could be found when using MR, MAR, and REST. For P1 latency, no significant difference among the four references was shown, whether in the CI or control group. Group difference in P1 latency could be found in MR and MAR. N1 amplitude in LMR was significantly lower than NR and MAR in the control group. LMR, MAR, and REST could distinguish the difference in the N1 amplitude between the CI and control group. Contralateral MR or MAR was found to be better in differentiating CI children versus controls. No group difference was found for the artifact component.Conclusions: Different references for AEP studies do not affect the CI artifact. In addition, contralateral MR is preferable for P1–N1 component studies involving CI children, as well as methodology-like studies.