The zero of potential

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

OBJECTIVE

Conventional 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.

METHODS

For EEG signals and power/connectivity configurations, simulation and clinical schizophrenia resting-state EEG datasets were used to investigate the performance of REST.

RESULTS

REST 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.

CONCLUSIONS

These results demonstrated that REST transformation could be adopted to resolve the offline rereferencing of clinical EEGs with specific nonunipolar mastoid references.

SIGNIFICANCE

REST 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.

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

Parkinson's disease is characterized by sub-second resting-state spatio-oscillatory patterns: A contribution from deep convolutional neural network

Deep convolutional neural network (DCNN) provides a multivariate framework to detect relevant spatio-oscillatory patterns in the data beyond common mass-univariate statistics. Yet, its practical application is limited due to the low interpretability of the results beyond accuracy. We opted to use DCNN with a minimalistic architecture design and large penalized terms to yield a generalizable and clinically relevant network model. Our network was trained based on the scalp topology of the electroencephalography (EEG) from an open access dataset, constituting our primary sample of healthy controls (n = 25) and Parkinson's disease (PD) patients (n = 25), with and without medication. Next, we validated the model on another independent, yet comparable open access EEG dataset (healthy controls (n = 20) and PD patients (n = 20)), which was unseen to the network. We applied Gradient-weighted Class Activation Mapping (Grad-CAM) interpretability technique to create a localization map exhibiting the key network predictors, based on the gradients of the classification score flowing into the last convolutional layer. Accordingly, our results indicated that a sub-second of intrinsic oscillatory power pattern in the beta band over the occipitoparietal, gamma band over the left motor cortex as well as theta band over the frontoparietal cluster, had the largest impact on the network score for dissociating the PD patients from age- and gender-matched healthy controls, across the two datasets. We further found that the off-medication motor symptoms were related to the occipitoparietal off-medication beta power whereas the disease duration was associated with the off-medication beta power of the motor cortex. The on-medication theta power of the frontoparietal was related to the improvement of the motor symptoms. In conclusion, our method enabled us to characterize PD patho-electrophysiology according to the multivariate topographic analysis approach, where both spatial and frequency aspects of the oscillations were simultaneously considered. Moreover, our approach was free from common reference problem of the EEG data analyses.

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 ( $0.06\pm 0.02$  μV for wave I, $0.07\pm 0.02$  μV for wave III, and $0.21\pm 0.04$  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.

Reference Electrode Standardization Interpolation Technique (RESIT): A Novel Interpolation Method for Scalp EEG

“Bad channels” are common phenomena during scalp electroencephalography (EEG) recording that arise due to various technique-related reasons, and reconstructing signals from bad channels is an inevitable choice in EEG processing. However, current interpolation methods are all based on purely mathematical interpolation theory, ignoring the neurophysiological basis of the EEG signals, and their performance needs to be further improved, especially when there are many scattered or adjacent bad channels. Therefore, a new interpolation method, named the reference electrode standardization interpolation technique (RESIT), was developed for interpolating scalp EEG channels. Resting-state and event-related EEG datasets were used to investigate the performance of the RESIT. The main results showed that (1) assuming 10% bad channels, RESIT can reconstruct the bad channels well; (2) as the percentage of bad channels increased (from 2% to 85%), the absolute and relative errors between the true and RESIT-reconstructed signals generally increased, and the correlations between the true and RESIT signals decreased; (3) for a range of bad channel percentages (2% ~ 85%), the RESIT had lower absolute error (approximately 2.39% ~ 33.5% reduction), lower relative errors (approximately 1.3% ~ 35.7% reduction) and higher correlations (approximately 2% ~ 690% increase) than traditional interpolation methods, including neighbor interpolation (NI) and spherical spline interpolation (SSI). In addition, the RESIT was integrated into the EEG preprocessing pipeline on the WeBrain cloud platform (https://webrain.uestc.edu.cn/). These results suggest that the RESIT is a promising interpolation method for both separate and simultaneous EEG preprocessing that benefits further EEG analysis, including event-related potential (ERP) analysis, EEG network analysis, and strict group-level statistics.

Localization of deep brain activity with scalp and subdural EEG

To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were ~70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around ~2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. SIGNIFICANCE STATEMENT: Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface.

A unified weighted minimum norm solution for the reference inverse problem in EEG

A well known problem in EEG recordings deals with the unknown potential of the reference electrode. In the last years several authors presented comparisons among the most popular solutions, the global conclusion being that the traditional Average Reference (AR) and the Reference Standardization Technique (REST) are the best approximations (Nunez, 2010; Kayser and Tenke, 2010; Liu et al., 2015; Chella et al., 2016). In this work we do not aim to further compare these techniques but to support the fact that both solutions can be derived from a general inverse problem formalism for reference estimation (Hu et al., 2019; Hu et al., 2018; Salido-Ruiz et al., 2011). Using the alternative approach of least squares, our findings are consistent with the theoretical findings in Hu et al. (2019) and Hu et al. (2018) showing that the AR is the minimum norm solution, while REST is a weighted minimum norm including some approximate propagation model. AR is thus a particular case of REST, which itself uses a particular formulation of the source estimation inverse problem. With a different derivation, we provide the additional powerful evidences to reinforce the cited findings.

Large-Scale Networks for Auditory Sensory Gating in the Awake Mouse

The amplitude of the brain response to a repeated auditory stimulus is diminished as compared to the response to the first tone (T1) for interstimulus intervals (ISI) lasting up to hundreds of milliseconds. This adaptation process, called auditory sensory gating (ASG), is altered in various psychiatric diseases including schizophrenia and is classically studied by focusing on early evoked cortical responses to the second tone (T2) using 500-ms ISI. However, mechanisms underlying ASG are still not well-understood. We investigated ASG in awake mice from the brainstem to cortex at variable ISIs (125–2000 ms) using high-density EEG and intracerebral recordings. While ASG decreases at longer ISIs, it is still present at durations (500–2000 ms) far beyond the time during which brain responses to T1 could still be detected. T1 induces a sequence of specific stable scalp EEG topographies that correspond to the successive activation of distinct neural networks lasting about 350 ms. These brain states remain unaltered if T2 is presented during this period, although T2 is processed by the brain, suggesting that ongoing networks of brain activity are active for longer than early evoked-potentials and are not overwritten by an upcoming new stimulus. Intracerebral recordings demonstrate that ASG is already present at the level of ventral cochlear nucleus (vCN) and inferior colliculus and is amplified across the hierarchy in bottom-up direction. This study uncovers the extended stability of sensory-evoked brain states and long duration of ASG, and sheds light on generators of ASG and possible interactions between bottom-up and top-down mechanisms.

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.

Semantic Features Reveal Different Networks During Word Processing: An EEG Source Localization Study

The neural principles behind semantic category representation are still under debate. Dominant theories mostly focus on distinguishing concrete from abstract concepts but, in such theories, divisions into categories of concrete concepts are more developed than for their abstract counterparts. An encompassing theory on semantic category representation could be within reach when charting the semantic attributes that are capable of describing both concept types. A good candidate are the three semantic dimensions defined by Osgood (potency, valence, arousal). However, to show to what extent they affect semantic processing, specific neuroimaging tools are required. Electroencephalography (EEG) is on par with the temporal resolution of cognitive behavior and source reconstruction. Using high-density set-ups, it is able to yield a spatial resolution in the scale of millimeters, sufficient to identify anatomical brain parcellations that could differentially contribute to semantic category representation. Cognitive neuroscientists traditionally focus on scalp domain analysis and turn to source reconstruction when an effect in the scalp domain has been detected. Traditional methods will potentially miss out on the fine-grained effects of semantic features as they are possibly obscured by the mixing of source activity due to volume conduction. For this reason, we have developed a mass-univariate analysis in the source domain using a mixed linear effect model. Our analyses reveal distinct networks of sources for different semantic features that are active during different stages of lexico-semantic processing of single words. With our method we identified differences in the spatio-temporal activation patterns of abstract and concrete words, high and low potency words, high and low valence words, and high and low arousal words, and in this way shed light on how word categories are represented in the brain.

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

BACKGROUND

The 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 METHODS

We 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.

RESULTS

We 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.

CONCLUSIONS

These 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.

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.

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.

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.

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.

MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG

Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies.

Electrophysiological Correlates of Change Detection during Delayed Matching Task: A Comparison of Different References

Detecting the changed information between memory representation and incoming sensory inputs is a fundamental cognitive ability. By offering the promise of excellent temporal resolution, event-related potential (ERP) technique has served as a primary tool for studying this process with reference of the linked mastoid (LM). However, given that LM may distort the ERP signals, it is still undetermined whether LM is the best reference choice. The goal of the current study was to systematically compare LM, reference electrode standardization technique (REST) and average reference (AR) for assessing the ERP correlates of change detection during a delayed matching task. Colored shapes were adopted as materials while both the task-relevant shape feature and -irrelevant color feature could be changed. The results of the ERP amplitude showed that both of the task-relevant and -conjunction feature changes elicited significantly more positive posterior P2 in REST and AR, but not in LM. Besides, significantly increased N270 was observed in task-relevant and -conjunction feature changes in both the REST and LM, but in the conjunction feature change in AR. Only the REST-obtained N270 revealed a significant increment in task-irrelevant feature change, which was compatible with the delayed behavioral performance. Statistical parametric scalp mapping (SPSM) results showed a left posterior distribution for AR, an anterior distribution for LM, and both the anterior and left posterior distributions for REST. These results indicate that different types of references may provide distinct cognitive interpretations. Interestingly, only the SPSM of REST was consistent with previous fMRI findings. Combined with the evidence of simulation studies and the current observations, we take the REST-based results as the objective one, and recommend using REST technology in the future ERP data analysis.

A Comparative Study on the Dynamic EEG Center of Mass with Different References

One of the most fundamental issues during an EEG study is choosing an available neutral reference. The infinity zero reference obtained by the reference electrode standardization technique (REST) has been recommended and used for its higher accuracy. This paper examined three traditional references, the average reference (AR), the linked mastoids reference (LM), and REST, in the study of the EEG center of mass (CM) using simulated and real ERPs. In the simulation, the relative error of REST was the smallest among the references. As for the ERP data with the visual oddball paradigm, the dynamic CM trajectory and its traveling velocity obtained by REST characterized three typical stages in spatial domain and temporal speed metrics, which provided useful information in addition to the distinct ERP waveform in the temporal domain. The results showed that the CM traveling from the frontal to parietal areas corresponding to the earlier positive components (i.e., P200 and P250), stays temporarily at the parietal area corresponding to P300 and then returns to the frontal area during the recovery stage. Compared with REST, AR, and LM not only changed the amplitude of P300 significantly but distorted the CM trajectory and its instantaneous velocity. As REST continues to provide objective results, we recommend that REST be used in future EEG/ERP CM studies.

How Different EEG References Influence Sensor Level Functional Connectivity Graphs

Highlights: Hamming Distance is applied to distinguish the difference of functional connectivity networkThe orientations of sources are testified to influence the scalp Functional Connectivity Graph (FCG) from different references significantlyREST, the reference electrode standardization technique, is proved to have an overall stable and excellent performance in variable situations. The choice of an electroencephalograph (EEG) reference is a practical issue for the study of brain functional connectivity. To study how EEG reference influence functional connectivity estimation (FCE), this study compares the differences of FCE resulting from the different references such as REST (the reference electrode standardization technique), average reference (AR), linked mastoids (LM), and left mastoid references (LR). Simulations involve two parts. One is based on 300 dipolar pairs, which are located on the superficial cortex with a radial source direction. The other part is based on 20 dipolar pairs. In each pair, the dipoles have various orientation combinations. The relative error (RE) and Hamming distance (HD) between functional connectivity matrices of ideal recordings and that of recordings obtained with different references, are metrics to compare the differences of the scalp functional connectivity graph (FCG) derived from those two kinds of recordings. Lower RE and HD values imply more similarity between the two FCGs. Using the ideal recording (IR) as a standard, the results show that AR, LM and LR perform well only in specific conditions, i.e., AR performs stable when there is no upward component in sources' orientation. LR achieves desirable results when the sources' locations are away from left ear. LM achieves an indistinct difference with IR, i.e., when the distribution of source locations is symmetric along the line linking the two ears. However, REST not only achieves excellent performance for superficial and radial dipolar sources, but also achieves a stable and robust performance with variable source locations and orientations. Benefitting from the stable and robust performance of REST vs. other reference methods, REST might best recover the real FCG of EEG. Thus, REST based FCG may be a good candidate to compare the FCG of EEG based on different references from different labs.

Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice

Bispectral analysis is a signal processing technique that makes it possible to capture the non-linear and non-Gaussian properties of the EEG signals. It has found various applications in EEG research and clinical practice, including the assessment of anesthetic depth, the identification of epileptic seizures, and more recently, the evaluation of non-linear cross-frequency brain functional connectivity. However, the validity and reliability of the indices drawn from bispectral analysis of EEG signals are potentially biased by the use of a non-neutral EEG reference. The present study aims at investigating the effects of the reference choice on the analysis of the non-linear features of EEG signals through bicoherence, as well as on the estimation of cross-frequency EEG connectivity through two different non-linear measures, i.e., the cross-bicoherence and the antisymmetric cross-bicoherence. To this end, four commonly used reference schemes were considered: the vertex electrode (Cz), the digitally linked mastoids, the average reference, and the Reference Electrode Standardization Technique (REST). The reference effects were assessed both in simulations and in a real EEG experiment. The simulations allowed to investigated: (i) the effects of the electrode density on the performance of the above references in the estimation of bispectral measures; and (ii) the effects of the head model accuracy in the performance of the REST. For real data, the EEG signals recorded from 10 subjects during eyes open resting state were examined, and the distortions induced by the reference choice in the patterns of alpha-beta bicoherence, cross-bicoherence, and antisymmetric cross-bicoherence were assessed. The results showed significant differences in the findings depending on the chosen reference, with the REST providing superior performance than all the other references in approximating the ideal neutral reference. In conclusion, this study highlights the importance of considering the effects of the reference choice in the interpretation and comparison of the results of bispectral analysis of scalp EEG.

A Comparative Study of Average, Linked Mastoid, and REST References for ERP Components Acquired during fMRI

In simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) studies, average reference (AR), and digitally linked mastoid (LM) are popular re-referencing techniques in event-related potential (ERP) analyses. However, they may introduce their own physiological signals and alter the EEG/ERP outcome. A reference electrode standardization technique (REST) that calculated a reference point at infinity was proposed to solve this problem. To confirm the advantage of REST in ERP analyses of synchronous EEG-fMRI studies, we compared the reference effect of AR, LM, and REST on task-related ERP results of a working memory task during an fMRI scan. As we hypothesized, we found that the adopted reference did not change the topography map of ERP components (N1 and P300 in the present study), but it did alter the task-related effect on ERP components. LM decreased or eliminated the visual working memory (VWM) load effect on P300, and the AR distorted the distribution of VWM location-related effect at left posterior electrodes as shown in the statistical parametric scalp mapping (SPSM) of N1. ERP cortical source estimates, which are independent of the EEG reference choice, were used as the golden standard to infer the relative utility of different references on the ERP task-related effect. By comparison, REST reference provided a more integrated and reasonable result. These results were further confirmed by the results of fMRI activations and a corresponding EEG-only study. Thus, we recommend the REST, especially with a realistic head model, as the optimal reference method for ERP data analysis in simultaneous EEG-fMRI studies.

Is the Surface Potential Integral of a Dipole in a Volume Conductor Always Zero? A Cloud Over the Average Reference of EEG and ERP

Currently, average reference is one of the most widely adopted references in EEG and ERP studies. The theoretical assumption is the surface potential integral of a volume conductor being zero, thus the average of scalp potential recordings might be an approximation of the theoretically desired zero reference. However, such a zero integral assumption has been proved only for a spherical surface. In this short communication, three counter-examples are given to show that the potential integral over the surface of a dipole in a volume conductor may not be zero. It depends on the shape of the conductor and the orientation of the dipole. This fact on one side means that average reference is not a theoretical 'gold standard' reference, and on the other side reminds us that the practical accuracy of average reference is not only determined by the well-known electrode array density and its coverage but also intrinsically by the head shape. It means that reference selection still is a fundamental problem to be fixed in various EEG and ERP studies.

Impact of the reference choice on scalp EEG connectivity estimation

OBJECTIVE

Several scalp EEG functional connectivity studies, mostly clinical, seem to overlook the reference electrode impact. The subsequent interpretation of brain connectivity is thus often biased by the choice of a non-neutral reference. This study aims at systematically investigating these effects.

APPROACH

As EEG reference, we examined the vertex electrode (Cz), the digitally linked mastoids (DLM), the average reference (AVE), and the reference electrode standardization technique (REST). As a connectivity metric, we used the imaginary part of the coherency. We tested simulated and real data (eyes-open resting state) by evaluating the influence of electrode density, the effect of head model accuracy in the REST transformation, and the impact on the characterization of the topology of functional networks from graph analysis.

MAIN RESULTS

Simulations demonstrated that REST significantly reduced the distortion of connectivity patterns when compared to AVE, Cz, and DLM references. Moreover, the availability of high-density EEG systems and an accurate knowledge of the head model are crucial elements to improve REST performance, with the individual realistic head model being preferable to the standard realistic head model. For real data, a systematic change of the spatial pattern of functional connectivity depending on the chosen reference was also observed. The distortion of connectivity patterns was larger for the Cz reference, and progressively decreased when using the DLM, the AVE, and the REST. Strikingly, we also showed that network attributes derived from graph analysis, i.e. node degree and local efficiency, are significantly influenced by the EEG reference choice.

SIGNIFICANCE

Overall, this study highlights that significant differences arise in scalp EEG functional connectivity and graph network properties, in dependence on the chosen reference. We hope that our study will convey the message that caution should be used when interpreting and comparing results obtained from different laboratories using different reference schemes.

Estimating a neutral reference for electroencephalographic recordings: the importance of using a high-density montage and a realistic head model

Objective. In electroencephalography (EEG) measurements, the signal of each recording electrode is contrasted with a reference electrode or a combination of electrodes. The estimation of a neutral reference is a long-standing issue in EEG data analysis, which has motivated the proposal of different re-referencing methods, among which linked-mastoid re-referencing (LMR), average re-referencing (AR) and reference electrode standardization technique (REST). In this study we quantitatively assessed the extent to which the use of a high-density montage and a realistic head model can impact on the optimal estimation of a neutral reference for EEG recordings. Approach. Using simulated recordings generated by projecting specific source activity over the sensors, we assessed to what extent AR, REST and LMR may distort the scalp topography. We examined the impact electrode coverage has on AR and REST, and how accurate the REST reconstruction is for realistic and less realistic (three-layer and single-layer spherical) head models, and with possible uncertainty in the electrode positions. We assessed LMR, AR and REST also in the presence of typical EEG artifacts that are mixed in the recordings. Finally, we applied them to real EEG data collected in a target detection experiment to corroborate our findings on simulated data. Main results. Both AR and REST have relatively low reconstruction errors compared to LMR, and that REST is less sensitive than AR and LMR to artifacts mixed in the EEG data. For both AR and REST, high electrode density yields low re-referencing reconstruction errors. A realistic head model is critical for REST, leading to a more accurate estimate of a neutral reference compared to spherical head models. With a low-density montage, REST shows a more reliable reconstruction than AR either with a realistic or a three-layer spherical head model. Conversely, with a high-density montage AR yields better results unless precise information on electrode positions is available. Significance. Our study is the first to quantitatively assess the performance of EEG re-referencing techniques in relation to the use of a high-density montage and a realistic head model. We hope our study will help researchers in the choice of the most effective re-referencing approach for their EEG studies.

Brain electrical source imaging in manic and depressive episodes of bipolar disorder

OBJECTIVE

Bipolar disorder (BD) electroencephalographic (EEG) studies have reported varying results. The present study compared EEG in BD during manic and depressive episodes, using brain electrical source imaging [standardized low-resolution electromagnetic tomography (sLORETA)] to assess the cortical spatial distribution of the sources of EEG oscillation frequencies.

METHODS

Two independent datasets (a total of 95 patients with bipolar I disorder, of whom 59 were female) were analyzed. Dataset #1 comprised 14 patients in a manic as well as a depressive episode. Dataset #2 comprised 26 patients in a manic episode and 55 patients in a depressive episode. From the head surface-recorded EEG, sLORETA cortical activity was computed in eight EEG frequency bands, and compared between mood states in both datasets. The results from the two datasets were combined using conjunction analysis.

RESULTS

Conjunction analysis yielded significant differences between mood states: In manic compared to depressive states, patients had lesser theta frequency band activity (right-hemispheric lateral lower prefrontal and anterior temporal, mainly Brodmann areas 13, 38, and 47), and greater beta-2 and beta-3 frequency band activity (extended bilateral prefrontal-to-parietal, mainly Brodmann area 6, and the cingulate).

CONCLUSIONS

The spatial organization of the brain's electrical oscillations differed in patients with BD between manic and depressive mood states. The brain areas implementing the main functions that show opposing abnormalities during manic and depressive episodes were affected by unduly increased or decreased activity (beta or theta). The discussion considers that facilitating (beta) or inhibiting (theta) electrical activity can in either case result in behavioral facilitation or inhibition, depending on the function of the brain area.

Zazen meditation and no-task resting EEG compared with LORETA intracortical source localization

Meditation is a self-induced and willfully initiated practice that alters the state of consciousness. The meditation practice of Zazen, like many other meditation practices, aims at disregarding intrusive thoughts while controlling body posture. It is an open monitoring meditation characterized by detached moment-to-moment awareness and reduced conceptual thinking and self-reference. Which brain areas differ in electric activity during Zazen compared to task-free resting? Since scalp electroencephalography (EEG) waveforms are reference-dependent, conclusions about the localization of active brain areas are ambiguous. Computing intracerebral source models from the scalp EEG data solves this problem. In the present study, we applied source modeling using low resolution brain electromagnetic tomography (LORETA) to 58-channel scalp EEG data recorded from 15 experienced Zen meditators during Zazen and no-task resting. Zazen compared to no-task resting showed increased alpha-1 and alpha-2 frequency activity in an exclusively right-lateralized cluster extending from prefrontal areas including the insula to parts of the somatosensory and motor cortices and temporal areas. Zazen also showed decreased alpha and beta-2 activity in the left angular gyrus and decreased beta-1 and beta-2 activity in a large bilateral posterior cluster comprising the visual cortex, the posterior cingulate cortex and the parietal cortex. The results include parts of the default mode network and suggest enhanced automatic memory and emotion processing, reduced conceptual thinking and self-reference on a less judgmental, i.e., more detached moment-to-moment basis during Zazen compared to no-task resting.

Functionally aberrant electrophysiological cortical connectivities in first episode medication-naive schizophrenics from three psychiatry centers

Functional dissociation between brain processes is widely hypothesized to account for aberrations of thought and emotions in schizophrenic patients. The typically small groups of analyzed schizophrenic patients yielded different neurophysiological findings, probably because small patient groups are likely to comprise different schizophrenia subtypes. We analyzed multichannel eyes-closed resting EEG from three small groups of acutely ill, first episode productive schizophrenic patients before start of medication (from three centers: Bern N = 9; Osaka N = 9; Berlin N = 12) and their controls. Low resolution brain electromagnetic tomography (LORETA) was used to compute intracortical source model-based lagged functional connectivity not biased by volume conduction effects between 19 cortical regions of interest (ROIs). The connectivities were compared between controls and patients of each group. Conjunction analysis determined six aberrant cortical functional connectivities that were the same in the three patient groups. Four of these six concerned the facilitating EEG alpha-1 frequency activity; they were decreased in the patients. Another two of these six connectivities concerned the inhibiting EEG delta frequency activity; they were increased in the patients. The principal orientation of the six aberrant cortical functional connectivities was sagittal; five of them involved both hemispheres. In sum, activity in the posterior brain areas of preprocessing functions and the anterior brain areas of evaluation and behavior control functions were compromised by either decreased coupled activation or increased coupled inhibition, common across schizophrenia subtypes in the three patient groups. These results of the analyzed three independent groups of schizophrenics support the concept of functional dissociation.

Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference

The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.

Whole-scalp EEG mapping of somatosensory evoked potentials in macaque monkeys

High-density scalp EEG recordings are widely used to study whole-brain neuronal networks in humans non-invasively. Here, we validate EEG mapping of somatosensory evoked potentials (SSEPs) in macaque monkeys (Macaca fascicularis) for the long-term investigation of large-scale neuronal networks and their reorganisation after lesions requiring a craniotomy. SSEPs were acquired from 33 scalp electrodes in five adult anaesthetized animals after electrical median or tibial nerve stimulation. SSEP scalp potential maps were identified by cluster analysis and identified in individual recordings. A distributed, linear inverse solution was used to estimate the intracortical sources of the scalp potentials. SSEPs were characterised by a sequence of components with unique scalp topographies. Source analysis confirmed that median nerve SSEP component maps were in accordance with the somatotopic organisation of the sensorimotor cortex. Most importantly, SSEP recordings were stable both intra- and interindividually. We aim to apply this method to the study of recovery and reorganisation of large-scale neuronal networks following a focal cortical lesion requiring a craniotomy. As a prerequisite, the present study demonstrated that a 300-mm² unilateral craniotomy over the sensorimotor cortex necessary to induce a cortical lesion, followed by bone flap repositioning, suture and gap plugging with calcium phosphate cement, did not induce major distortions of the SSEPs. In conclusion, SSEPs can be successfully and reproducibly recorded from high-density EEG caps in macaque monkeys before and after a craniotomy, opening new possibilities for the long-term follow-up of the cortical reorganisation of large-scale networks in macaque monkeys after a cortical lesion.

Why do we need to use a zero reference? Reference influences on the ERPs of audiovisual effects

Using ERPs in the audiovisual stimulus, the current study is the first to investigate the influence of the reference on experimental effects (between two conditions). Three references, the average reference (AR), the mean mastoid (MM), and a new infinity zero reference (IR), were comparatively investigated via ERPs, statistical parametric scalp mappings (SPSM), and LORETA. Specifically, for the N1 (170-190 ms), the SPSM results showed an anterior distribution for MM, a posterior distribution for IR, and both anterior and posterior distributions for AR. However, the circumstantial evidence provided by LORETA is consistent with SPSM of IR. These results indicated that the newly developed IR could provide increased accuracy; thus, we recommend IR for future ERP studies.

How local is the local field potential?

Local field potentials (LFPs) are of growing importance in neurophysiological investigations. LFPs supplement action potential recordings by indexing activity relevant to EEG, magnetoencephalographic, and hemodynamic (fMRI) signals. Recent reports suggest that LFPs reflect activity within very small domains of several hundred micrometers. We examined this conclusion by comparing LFP, current source density (CSD), and multiunit activity (MUA) signals in macaque auditory cortex. Estimated by frequency tuning bandwidths, these signals' "listening areas" differ systematically with an order of MUA < CSD < LFP. Computational analyses confirm that observed LFPs receive local contributions. Direct measurements indicate passive spread of LFPs to sites more than a centimeter from their origins. These findings appear to be independent of the frequency content of the LFP. Our results challenge the idea that LFP recordings typically integrate over extremely circumscribed local domains. Rather, LFPs appear as a mixture of local potentials with "volume conducted" potentials from distant sites.

Use of swLORETA to localize the cortical sources of target- and distracter-elicited P300 components

OBJECTIVE

Cognitive event-related potentials (especially P300) have long been used to explore attentional processes. The aim of this study was to identify the cortical areas involved in P300 generation during a selective attention task.

METHODS

128 channel electroencephalograms were recorded in 15 healthy controls performing a three-stimulus visual oddball paradigm, in order to identify distracter- and target-elicited P300 components. For each subject, the P300 sources were localized using standardized weighted low-resolution electromagnetic tomography (swLORETA). One sample and paired T-tests were performed using SPM5®.

RESULTS

Common sources for both P300 components were observed within a large frontoparietal network, including the frontal eye field and dorsal parietal cortex (i.e. the attentional dorsal frontoparietal network). More inferior parietal areas, prefrontal and cingulate cortices (i.e. the attentional ventral frontoparietal network) were also involved in the generation of target-elicited P300.

CONCLUSIONS

These results suggest that distracter- and target-elicited P300 are both generated by the dorsal frontoparietal network. Moreover, target processing recruits a specific ventral network.

SIGNIFICANCE

Our data agree with the literature reports using other methods and should help to improve our knowledge of the cerebral networks underlying attentional processes.

Spatiotemporal analysis of multichannel EEG: CARTOOL

This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way.

The timing of temporoparietal and frontal activations during mental own body transformations from different visuospatial perspectives

The perspective from where the world is perceived is an important aspect of the bodily self and may break down in neurological conditions such as out-of-body experiences (OBEs). These striking disturbances are characterized by disembodiment, an external perspective and have been observed after temporoparietal damage. Using mental own body imagery, recent neuroimaging work has linked perspectival changes to the temporoparietal cortex. Because the disembodied perspective during OBEs is elevated in the majority of cases, we tested whether an elevated perspective will interfere with such temporoparietal mechanisms mental own body imagery. We designed stimuli of life-sized humans rotated around the vertical axis and rendered as if viewed from three different perspectives: elevated, lowered, and normal. Reaction times (RTs) in an own body transformation task, but not the control condition, were dependent on the rotation angle. Furthermore, RTs were shorter for the elevated as compared with the normal or lowered perspective. Using high-density EEG and evoked potential (EP) mapping, we found a bilateral temporoparietal and frontal activation at approximately 330-420 ms after stimulus onset that was dependent on the rotation angle, but not on the perspective. This activation was also found in response-locked EPs. In the time period approximately 210-330 ms we found a temporally distinct posterior temporal activation with its duration being dependent on the perspective, but not the rotation angle. Collectively, the present findings suggest that temporoparietal and frontal as well as posterior temporal activations and their timing are crucial neuronal correlates of the bodily self as studied by mental imagery.

A mouse model for studying large-scale neuronal networks using EEG mapping techniques

Human functional imaging studies are increasingly focusing on the identification of large-scale neuronal networks, their temporal properties, their development, and their plasticity and recovery after brain lesions. A method targeting large-scale networks in rodents would open the possibility to investigate their neuronal and molecular basis in detail. We here present a method to study such networks in mice with minimal invasiveness, based on the simultaneous recording of epicranial EEG from 32 electrodes regularly distributed over the head surface. Spatiotemporal analysis of the electrical potential maps similar to human EEG imaging studies allows quantifying the dynamics of the global neuronal activation with sub-millisecond resolution. We tested the feasibility, stability and reproducibility of the method by recording the electrical activity evoked by mechanical stimulation of the mystacial vibrissae. We found a series of potential maps with different spatial configurations that suggested the activation of a large-scale network with generators in several somatosensory and motor areas of both hemispheres. The spatiotemporal activation pattern was stable both across mice and in the same mouse across time. We also performed 16-channel intracortical recordings of the local field potential across cortical layers in different brain areas and found tight spatiotemporal concordance with the generators estimated from the epicranial maps. Epicranial EEG mapping thus allows assessing sensory processing by large-scale neuronal networks in living mice with minimal invasiveness, complementing existing approaches to study the neurophysiological mechanisms of interaction within the network in detail and to characterize their developmental, experience-dependent and lesion-induced plasticity in normal and transgenic animals.

Topographic ERP analyses: a step-by-step tutorial review

In this tutorial review, we detail both the rationale for as well as the implementation of a set of analyses of surface-recorded event-related potentials (ERPs) that uses the reference-free spatial (i.e. topographic) information available from high-density electrode montages to render statistical information concerning modulations in response strength, latency, and topography both between and within experimental conditions. In these and other ways these topographic analysis methods allow the experimenter to glean additional information and neurophysiologic interpretability beyond what is available from canonical waveform analyses. In this tutorial we present the example of somatosensory evoked potentials (SEPs) in response to stimulation of each hand to illustrate these points. For each step of these analyses, we provide the reader with both a conceptual and mathematical description of how the analysis is carried out, what it yields, and how to interpret its statistical outcome. We show that these topographic analysis methods are intuitive and easy-to-use approaches that can remove much of the guesswork often confronting ERP researchers and also assist in identifying the information contained within high-density ERP datasets.

Automatic identification and removal of scalp reference signal for intracranial EEGs based on independent component analysis

The pursuit of an inactive recording reference is one of the oldest technical problems in electroencephalography (EEG). Since commonly used cephalic references contaminate EEG and can lead to misinterpretation, extraction of the reference contribution is of fundamental interest. Here, we apply independent component analysis (ICA) to intracranial recordings and propose two methods to automatically identify and remove the reference based on the assumption that the scalp reference is independent from the local and distributed intracranial sources. This assumption, supported by our results, is generally valid because the reference scalp electrode is relatively electrically isolated from the intracranial electrodes by the skull's high resistivity. We point out that the linear model is underdetermined when the reference is considered as a source, and discuss one special underdetermined case for which a unique class of outputs can be separated. For this case most ICA algorithms can be applied, and we argue that intracranial or scalp EEGs follow this special case. We apply the two proposed methods to intracranial EEGs from three patients undergoing evaluation for epilepsy surgery, and compare the results to bipolar and average reference recordings. The proposed methods should have wide application in quantitative EEG studies.

The effect of reference choices on the spatio-temporal analysis of brain evoked potentials: the use of infinite reference

Reference is a very virtual issue in EEG and ERP. Understanding the difference of various references will make the applications more confident. In this work, somatosensory evoked potential (SEP) with stimulation on the right hand was studied. The SEP spatio-temporal analysis was conducted comparatively on six references, left mastoid (contralateral mastoid reference, CM), right mastoid (ipsilateral mastoid reference, IM), linked mastoids (LM), average reference (AR), vertex reference (Cz) and the infinity reference (IR) newly proposed in 2001. Among the six, CM is the one used in actual recordings, and the other five are obtained by off-line re-referencing. The comparison is conducted on four selected components (P30 ms, P40 ms, N90 ms and P230 ms) in both temporal and spatial aspects. The results show that references may have a distinct influence on the amplitudes of the scalp potentials, with relative error at some electrodes larger than 500%, and for some electrodes it may even change the polarity. Pair-wise multiple comparison (Tukey test) shows that the differences of peak values among various references are very significant (P<0.001) between Cz and IR\CM\IM\LM, and significant (P<0.01) between Cz and AR for component N90 ms; very significant (P<0.001) between Cz and IR\CM\IM\LM\AR, significant between IMLM and AR (P<0.01), CM and AR (P<0.05) for component P230 ms. The amplitude value order is CM/IM> or =LM>IR>AR>Cz. The two-ways (the six references vs. the four Peaks) repeated measures ANOVA test shows the effect of different references depends on various components; there is a statistically significant interaction between reference and the peak (P=<0.001). While for the spatial map of the potential amplitude, references will not affect the amplitude map shape if the color-bar is selected automatically, but if a fixed color-bar is chosen for data of various references, they may show some differences. These results mean a common reference is important for producing a comparable result between labs. As IR is theoretically a constant reference, we recommend it as the common choice in the future.