1. EEG Acquisition
<ol>
	<li>Explain the experimental procedures to the participant and obtain informed consent.</li>
	<li>Apply drop-down electrodes. Clean area on the face where drop-down electrodes will be located using an alcohol swab.</li>
	<li>Place electrode cap on the participant's head. Measure participant's head circumference and choose the appropriate cap size. Following the internationally recognized 10-20 system for electrode placement, measure the distance from nasion to inion along the midline and divide by 10%. Using that number, measure up from nasion and mark. Align the electrode cap position Fp with this mark and pull the cap back. Make sure that the center of the cap is in line with the nose. Measure nasion to Cz, and confirm that this distance is half the distance from nasion to inion. Tighten the chinstrap.</li>
	<li>Place gel-filled blunt-point syringe in the electrode holders. To create a conductive column of gel, start in contact with the scalp, then squeeze and pull back. Note that the application of too much gel may bridge the signals of neighboring electrodes.</li>
	<li>Fix active electrodes into the electrode holders.</li>
	<li>Position the subject in front of the monitor at the appropriate distance for the experiment. Ask the participant to remain still, emphasizing the importance of minimizing eye movements and blinks for a clean recording.</li>
	<li>Examine the electrode connections and EEG signal quality on the acquisition computer. Verify that all electrode offsets are low (&lt; 40 mV) and stable. If there is a problem with a particular electrode, take out that electrode and reapply gel to adjust impedances at that site.</li>
	<li>Save the file and start the experiment.</li>
</ol>
2. EEG Analysis
<ol>
	<li>After experimentation, but before extracting the particular statistic of interest, preprocess the continuous EEG data to remove artifacts using standard procedures of filtering and artifact rejection. Cut the continuous EEG into epochs corresponding to each discrete event, such as the presentation of photograph. In each epoch, include a 100 msec pre-stimulus window as a baseline.</li>
	<li>Event-related potentials (ERP) analysis captures synchronous brain activity that is phase-locked to the onset of the event. Average across trials to separate the evoked responses from the "noisy" (i.e. non-phase locked) background activity. The variability across trials and between-subjects presents a major challenge for the ERP method of analysis. To achieve a good signal-to-noise ratio the experimental protocol should include many discrete events with definable onsets. Time locking the brain's response to the onset of a salient event and then averaging over many like events helps to reduce some of this noise; however, the temporal synchrony created by this procedure typically dissolves within 1 sec. Identify ERP component peak amplitudes and latencies for each subject (for more detailed guidelines on ERP analysis, see Picton et al., 2000).</li>
	<li>Using Fourier analysis, transform the EEG signal from the time domain to the frequency domain and decompose the signal into its composite sine waves of varying frequencies 6.</li>
	<li>Multiscale entropy (MSE) is an information theoretic metric that estimates the variability of the neuroelectical signals over time and across multiple timescales. To provide a conceptual depiction of MSE analysis, consider two simulated waveforms, a regular waveform and a more stochastic one. Sample entropy values are near zero for the regular waveform and ~2.5 for the more variable waveform. An increase in sample entropy corresponds to an increase in signal complexity, which, according to information theory, can be interpreted as an increase in the amount of information processing capacity of the underlying system 7,16. Remember that the capacity of a brain is not fixed but changes depending on the neural context 2, i.e. the brain regions that happen to be functionally connected at a particular point in time.</li>
	<li>To calculate MSE, use the algorithm available at <a href="http://www.physionet.org/physiotools/mse/">www.physionet.org/physiotools/mse/</a>, which computes MSE in two steps.</li>
	<li>First, the algorithm progressively down-samples the EEG time series per trial and per condition. Down-sample the original time series to generate multiple time series of varying timescales. Time series 1 is the original time series. To create the time series of subsequent timescales, divide the original time series into non-overlapping windows of the timescale length and average the data points within each window. Down-sampling is similar to low-pass filtering; dividing the sampling frequency by the timescale will approximate the frequency at which the signal is low-pass filtered for that particular timescale. The application of MSE to a particular frequency range (e.g., alpha: 9 Hz to 12 Hz) can be interpreted as representing the composition of rhythms within that range as well as the interaction between those frequencies.</li>
	<li>Second, the algorithm calculates the sample entropy for each coarse-grained time series 14. Sample entropy estimates the complexity of a time series. In a nonlinear analysis of EEG, one assumes that an individual time series represents the manifestation of an underlying multi-dimensional non-linear dynamic model (see Stam, 2005 for a review). In this example, m (the pattern length) is set to two, which means that the variability of the amplitude pattern of each time series will be represented in two-dimensional versus three-dimensional space by considering the sequence pattern of two versus three consecutive data points, respectively. Parameter r (the similarity criterion), reflects the amplitude range (denoted by the height of the colored bands) within which data points are considered to "match". For a typical EEG time series with more than 100 data points, set the parameter m equal to 2 and the parameter r equal to a value between 0.5 and 1 (see Richman and Moorman, 2000; for a detailed procedure on selecting parameters refer to Lake et al., 2002). 
	To calculate sample entropy for this simulated time series, begin with the first two-component sequence pattern, red-orange. First, count the number of times the red-orange sequence pattern occurs in the time series; there are 10 matches for this two-component sequence. Second, count the number of times first three-component sequence pattern, red-orange-yellow, occurs in the time series; there are 5 matches for this three-component sequence. Continue with the same operations for the next two-component sequence (orange-yellow) and the next three-component sequence (orange-yellow-green) of the time series. The number of two-component matches (5) and three-component matches (3) for these sequences are added to the previous values (total two-component matches = 15; total three-component matches = 8). Repeat for all other sequence matches in the time series (up to N - m) to determine the total ratio of two-component matches to three-component matches. Sample entropy is the natural logarithm of this ratio. For each subject, compute the channel specific MSE estimate as the mean across single trial entropy measures for each timescale.</li>
</ol>

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R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+neural+context+in+large-scale+neurocognitive+network+operations.">The role of neural context in large-scale neurocognitive network operations.</a> Springer Handbook on Brain Connectivity. , 403-419 (2007).</li><li>Buzsaki, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Rhythms of the brain. , (2006).</li><li>Costa, M., Goldberger, A., Peng, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiscale+entropy+analysis+of+biological+signals.">Multiscale entropy analysis of biological signals.</a> Phys. Rev. E. 712, 1-18 (2005).</li><li>Deco, G., Jirsa, V., McIntosh, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Emerging+concepts+for+the+dynamical+organization+of+resting-state+activity+in+the+brain.">Emerging concepts for the dynamical organization of resting-state activity in the brain.</a> Nat. Rev. Neurosci. 12, 43-56 (2011).</li><li>Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+labile+brain.+I.+Neuronal+transients+and+nonlinear+coupling.">The labile brain. I. Neuronal transients and nonlinear coupling.</a> Philos. Trans. R. Soc. Lond. B. Biol. Sci. 355, 215-236 (2001).</li><li>Gatlin, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Information Theory and the Living System. , (1972).</li><li>Heisz, J. J., Shedden, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Semantic+learning+modifies+perceptual+face+processing.">Semantic learning modifies perceptual face processing.</a> Journal of Cognitive Neuroscience. 21, 1127-1134 (2009).</li><li>Heisz, J. J., Shedden, J. M., McIntosh, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Relating+brain+signal+variability+to+knowledge+representation.">Relating brain signal variability to knowledge representation.</a> NeuroImage. 63, 1384-13 (2012).</li><li>Lake, D. E., Richman, J. S., Griffin, P., Moorman, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sample+entropy+analysis+of+neonatal+heart+rate+variability.">Sample entropy analysis of neonatal heart rate variability.</a> Am. J. Physiol. Regul. Integr. Comp. Physiol. 283, R789-R797 (2002).</li><li>Lobaugh, N. J., West, R., McIntosh, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatiotemporal+analysis+of+experimental+differences+in+event-related+potential+data+with+partial+least+squares.">Spatiotemporal analysis of experimental differences in event-related potential data with partial least squares.</a> Psychophysio. 38, 517-530 (2001).</li><li>McIntosh, A. R., Kovacevic, N., Itier, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increased+brain+signal+variability+accompanies+behavioral+variability+in+development.">Increased brain signal variability accompanies behavioral variability in development.</a> PLoS Computational Biology. 4, 7 (2008).</li><li>Picton, T. W., Bentin, S., Berg, P., Donchin, E., Hillyard, S. A., Johnson, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guidelines+for+using+human+event-related+potentials+to+study+cognition:+Recording+standards+and+publication+criteria.">Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria.</a> Psychophysiology. 37, 127-152 (2000).</li><li>Richman, J. S., Moorman, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physiological+time+series+analysis+using+approximate+entropy+and+sample+entropy.">Physiological time series analysis using approximate entropy and sample entropy.</a> Am. J. Physiol. Heart Circ Physiol. 278, H2039-H2049 (2000).</li><li>Rossion, B., Jacques, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Does+physical+interstimulus+variance+account+for+early+electrophysiological+face+sensitivity+responses+in+the+human+brain?+Ten+lessons+on+the+N170.">Does physical interstimulus variance account for early electrophysiological face sensitivity responses in the human brain? Ten lessons on the N170.</a> NeuroImage. 39, 1959-1979 (2008).</li><li>Shannon, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Mathematical+Theory+of+Communication.">A Mathematical Theory of Communication.</a> The Bell System Technical Journal. 27, 379-423 (1948).</li><li>Stam, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonlinear+dynamical+analysis+of+EEG+and+MEG:+review+of+an+emerging+field.">Nonlinear dynamical analysis of EEG and MEG: review of an emerging field.</a> Clinical Neurophysiology. 116, 2266-2301 (2005).</li><li>Vakorin, V. A., McIntosh, A. R., Rabinovich, M. I., Friston, K. 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No conflicts of interest declared.

The goal of the present article was to provide a conceptual and methodological description of multiscale entropy (MSE) as it applies to EEG neuroimaging data. EEG is a powerful non-invasive neuroimaging technique that measures neural network activity with the high temporal resolution. The EEG signal reflects post-synaptic activity of populations of pyramidal cells in the cortex, whose collective responses are modified by various excitatory and inhibitory reentrant connections. Accordingly, there are multiple ways to analyze EEG data and each method extracts a unique aspect of the data.
We discussed two common methods of analysis: event-related potential (ERP) analysis and spectral power analysis. ERP analysis captures the synchronous neuronal activity in the EEG signal that is phase-locked to the onset of a discrete event. ERPs reflect specific perceptual, motor, or cognitive operations, making this statistic ideal for examining specific processing stages. Spectral power analysis quantifies the relative contribution of a particular frequency to the EEG signal. Various excitatory and inhibitory feedback loops interact to entrain the activity of neuronal populations at a particular frequency 1,3. Such synchrony between disparate brain regions is thought to promote the binding of information across widespread neural networks. There is a rich literature supporting the link between the power within a particular frequency range and a specific emotional or cognitive state of function 3.
When analyzing EEG it is also important to keep in mind that neural networks are complex systems with non-linear dynamics. Such complexity is reflected in the EEG signal as irregular oscillations that are not the consequence of meaningless background noise. Like synchronous oscillatory activity, the interactions between various excitatory and inhibitory reentrant loops cause transient fluctuations in the brain signal over time 6. Such transients are believed to reflect transitions or bifurcations between network microstates that can be used to estimate the degrees of freedom or complexity of the underlying network; greater variability in the amplitude pattern of the signal over time is indicative of a more complex system 5. Critically, ERP or spectral power analyses are not sensitive to such irregular activity, whereas MSE is. Moreover, an index of network complexity cannot be obtained by simply counting the number of active brain regions as such a method is blind to the transient and dynamic recurrent interactions between brain regions.
Complementary methods for neuroimaging analysis combine to create a complete picture of the underlying neural activity. The interpretation of results from more traditional applications of neuroimaging data, such as ERP and spectral power, are augmented by measures of complexity like MSE; MSE provides a way to capture the sequence of changes in the spatiotemporal patterns of brain activity across multiple timescales that contributes to a specific cognitive operation. Applying MSE to new and existing data sets may provide further insight into how cognition emerges from neural network dynamics.

Recent advances in neuroimaging have dramatically augmented our understanding of brain function. However, many of the applications of neuroimaging data tend to reinforce the view of the brain in static states rather than emphasizing cognitive operations as they unfold in real time. Consequently, little is known about the space-time structure of brain networks and how the sequence of changes in spatiotemporal patterns across multiple timescales contributes to a specific cognitive operation. The present article describes multiscale entropy (MSE) 5, a new analytic tool for neuroimaging data that examines the complexity of the spatiotemporal pattern underlying specific cognition operations by providing information about how different neural generators in a functional brain network communicate across multiple timescales.
Derived from information theory, an applied branch of mathematics 7,16, MSE was originally designed to examine the complexity of electrocardiograms 4. In theory, MSE could be used to analyze the complexity of any time series; the primary requisite is that the signal time series contains at least 50 data points of continuous time. However, the timescale dependence and sensitivity to linear and nonlinear dynamics in the data may make MSE particularly informative of neural network dynamics.
Here, we focus on the application of MSE to electroencephalogram (EEG) neuroimaging data 9,12. EEG is a noninvasive neuroimaging technique whereby electrodes that are placed on the scalp capture the postsynaptic responses of populations of neurons in neocortex 1. With high temporal resolution, EEG easily meets the time series length requisite of MSE without altering the typical acquisition protocol. To emphasize the utility of the application of MSE to EEG data, we compare this novel method with more traditional approaches including event-related potential and spectral power. When used together, these complementary methods of analysis provide a more complete description of the data that may lead to further insight into neural network operations that give rise to cognition.

<table><tbody><tr><td>EEG </td><td>BioSemi</td><td></td><td></td></tr></tbody></table>

applications of eeg neuroimaging data: event-related potentials, spectral power, and multiscale entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.

Figures 1A and 2A represent the EEG signal in response to the presentation of a face image. Averaging across like trials produces an ERP waveform that consists of a series of positive and negative deflections called ERP components. Figure 1B illustrates an averaged waveform for a single subject and Figure 6A illustrates a grand average waveform for a group of subjects. There is a rich literature that relates each ERP component to a specific perceptual, motor, or cognitive operation. For example, the N170 is a negative deflection that peaks at approximately 170 msec post-stimulus onset and is implicated in face processing 8,15.
Figure 2B illustrates the decomposition of that same EEG signal into component frequency bands. The results from spectral power analysis reveal the frequency content of the signal (Figure 2C), whereby an increase in power at a particular frequency reflects an increase in the presence of that rhythm within the EEG signal.
Like spectral power, MSE is sensitive to the complexity of the oscillatory components contributing to the signal. However, unlike spectral power, MSE is also sensitive to the interactions between frequency components (i.e. nonlinear dynamics 18). The complexity of an EEG signal is represented as a function of sample entropy (Figure 5) over multiple timescales (Figure 4). As illustrated in Figure 3, sample entropy is low for regular signals and increases with the degree of signal randomness. Unlike traditional entropy measures that increase with degree of randomness, multiscale entropy is able to differentiate complex signals from white noise by considering entropy across multiple timescales. For example, Costa et al., 2005 compared multiscale entropy values for uncorrelated (white) noise versus correlated (pink) noise. While sample entropy was greater for white noise than pink noise at fine timescale, the opposite was observed at coarser timescales 5-20. In other words, when entropy was considered across multiple timescales, the true complexity of the signals was more accurately represented than would be if only a single timescale was considered. Depending of the temporal dynamics of a specific contrast, condition effects may be expressed: 1) in the same way across all timescales, 2) at some timescales but not other, or 3) as crossover effects whereby the contrast is different at fine versus coarser timescales.
Figure 6 depicts condition differences in ERP (Figure 6A), spectral power (Figure 6B), MSE (Figure 6C) contrasting the initial versus repeat presentations of face photographs 9. In this example all measures converged to reveal the same effect; however, the observed decrease in sample entropy that accompanies face repetition is important as it constrains the interpretation of the results. A decrease in complexity suggests that the underlying functional network is simpler and capable of processing less information.
Figure 7 depicts statistical results from the multivariate analysis of partial-least squares 11 applied to ERP, spectral power and MSE. The experiment manipulated the familiarity associated with different faces (Heisz et al., 2012). The contrast (bar graph) shows that ERP amplitude distinguished new faces from familiar faces but not among the familiar faces that varied in amount of prior exposure. Spectral power distinguished faces according to acquired familiarity but did not accurately distinguish between the faces of medium and low familiarity. MSE was most sensitive to the condition differences in that sample entropy values increased with increasing face familiarity. The image plots capture the spatiotemporal distribution of the condition effect across all electrodes and time/frequency/timescale. This example demonstrates a situation in which the analysis of EEG by MSE produced unique information that was not obtained using traditional methods of ERP or spectral power. This divergence of MSE suggests that the conditions differ with respect to nonlinear aspects of their network dynamics, possibly involving the interactions between various frequency components.
<img alt="Figure 1" src="/files/ftp_upload/50131/50131fig1.jpg" /> 
Figure 1. A) The EEG responses of a single subject as a function of amplitude deflection from baseline for each trial plotted against time from the onset of the trial. Each trial consisted of the presentation of a photograph of a face image. Positive amplitude deflections are depicted in red; negative amplitude deflections are depicted in blue. All trials show a positive deflection around 100 msec and 250 msec, indicating event-related phase-locked activity. B) Averaging across all trials depicted in Figure 1A produces an averaged ERP waveform with distinct positive and negative deflections called event-related components and named according to a standard nomenclature. For example, P1 is the first positive going component, and N170 is a negative component that peaks at approximately 170 msec post-stimulus onset.
<img alt="Figure 2" src="/files/ftp_upload/50131/50131fig2.jpg" /> 
Figure 2. A) The EEG response of a single subject for a single trial plotting amplitude by time (in data points, sampling rate 512 Hz). B) The EEG response of Figure 2A bandpass filtered to isolate frequency bands of delta (0-4 Hz), theta (5-8 Hz), alpha (9-12 Hz), beta (13-30 Hz) and gamma (&gt; 30 Hz). C) Spectral power density of the EEG response depicted in Figure 2A representing frequency composition of the signal as a function of power by frequency. An increase in spectral power at a particular frequency reflects an increase in the number of synchronously active neurons entrained within that particular frequency band. <a href="https://www.jove.com/files/ftp_upload/50131/50131fig2large.jpg" target="_blank">Click here to view larger figure</a>.
<img alt="Figure 3" src="/files/ftp_upload/50131/50131fig3.jpg" /> 
Figure 3. A) Two simulated waveforms: a regular or predictable waveform depicted in purple, and a more stochastic waveform depicted in black. B) Sample entropy values of the two simulated waveforms for the first three timescales. Sample entropy is low for highly predictable signals than more stochastic signals. <a href="https://www.jove.com/files/ftp_upload/50131/50131fig3large.jpg" target="_blank">Click here to view larger figure</a>.
<img alt="Figure 4" src="/files/ftp_upload/50131/50131fig4.jpg" /> 
Figure 4. Down-sampling the original time series generates multiple time series of varying timescales. Timescale 1 is the original time series. The time series of timescale 2 is created by dividing the original time series into non-overlapping windows of length 2 and average the data points within each window. To generate the time series of subsequent timescales, divide the original time series into non-overlapping windows of the timescale length and average the data points within each window.
<img alt="Figure 5" src="/files/ftp_upload/50131/50131fig5.jpg" /> 
Figure 5. A simulated waveform where each rectangle represents a single data point in the time series. Sample entropy estimates the variability of a time series. In this example, m (the pattern length) is set to two, which means that the variance of the amplitude pattern of each time series will be represented in two-dimensional versus three-dimensional space by considering the sequence pattern of two versus three consecutive data points, respectively; r (the similarity criterion), reflects the amplitude range (denoted by the height of the colored bands) within which data points are considered to "match". To calculate sample entropy for this simulated time series, begin with the first two-component sequence pattern, red-orange. First, count the number of times the red-orange sequence pattern occurs in the time series; there are 10 matches for this two-component sequence. Second, count the number of times first three-component sequence pattern, red-orange-yellow, occurs in the time series; there are 5 matches for this three-component sequence. Continue in this manner for the next two-component sequence (orange-yellow) and three-component sequence (orange-yellow-green). The number of two-component matches (5) and three-component matches (3) for these sequences are added to the previous values (total two-component matches =15; total three-component matches = 8). Repeat for all other sequence matches in the time series (up to N - m) to determine the total ratio of two-component matches to three-component matches. Sample entropy is the natural logarithm of this ratio. For each subject, compute the channel specific MSE estimate as the mean across single trial entropy measures for each timescale.
<img alt="Figure 6" src="/files/ftp_upload/50131/50131fig6.jpg" /> 
Figure 6. Condition differences in ERP (A), spectral power (B), MSE (C) contrasting the initial versus repeated presentations of facial photographs. <a href="https://www.jove.com/files/ftp_upload/50131/50131fig6large.jpg" target="_blank">Click here to view larger figure</a>.
<img alt="Figure 7" src="/files/ftp_upload/50131/50131fig7.jpg" /> 
Figure 7. Contrasting the EEG response to learned faces across measures of ERP, spectral power, and multiscale entropy. The bar graphs depict the contrast between conditions as determined by partial least squares analysis 11. The image plot highlights the spatial-temporal distribution at which this contrast was most stable as determined by bootstrapping. Values represent ~z scores and negative values denote significance for the inverse condition effect. <a href="https://www.jove.com/files/ftp_upload/50131/50131fig7large.jpg" target="_blank">Click here to view larger figure</a>.

Watch this Scientific Journal Video about Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy at JoVE.com

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain ...

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33.6K Views.  Baycrest. Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.

Video: Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Investigating the Effects of Antipsychotics and Schizotypy on the N400 Using Event-Related Potentials and Semantic Categorization

Within the field of cognitive neuroscience, functional magnetic resonance imaging (fMRI) is a popular method of visualizing brain function. This is in part because of its excellent spatial resolution, which allows researchers to identify brain areas associated with specific cognitive processes. However, in the quest to localize brain functions, it is relevant to note that many cognitive, sensory, and motor processes have temporal distinctions that are imperative to capture, an aspect that is left unfulfilled by fMRI&#8217;s suboptimal temporal resolution. To better understand cognitive processes, it is thus advantageous to utilize event-related potential (ERP) recording as a method of gathering information about the brain. Some of its advantages include its fantastic temporal resolution, which gives researchers the ability to follow the activity of the brain down to the millisecond. It also directly indexes both excitatory and inhibitory post-synaptic potentials by which most brain computations are performed. This sits in contrast to fMRI, which captures an index of metabolic activity. Further, the non-invasive ERP method does not require a contrast condition: raw ERPs can be examined for just one experimental condition, a distinction from fMRI where control conditions must be subtracted from the experimental condition, leading to uncertainty in associating observations with experimental or contrast conditions. While it is limited by its poor spatial and subcortical activity resolution, ERP recordings&#8217; utility, relative cost-effectiveness, and associated advantages offer strong rationale for its use in cognitive neuroscience to track rapid temporal changes in neural activity. In an effort to foster increase in its use as a research imaging method, and to ensure proper and accurate data collection, the present article will outline &#8211; in the framework of a paradigm using semantic categorization to examine the effects of antipsychotics and schizotypy on the N400 &#8211; the procedure and key aspects associated with ERP data acquisition.

Using event-related EEG potentials (ERPs), we investigate the effects of antipsychotic medications on abnormal semantic brain activations in healthy individuals with schizotypal traits. We use ERPs to track distinct changes in brain activity, shedding insight into the cognitive processes associated with semantic categorization.

Within the field of cognitive neuroscience, functional magnetic resonance imaging (fMRI) is a popular method of visualizing brain function. This is in ...

Behavior

Infant Auditory Processing and Event-related Brain Oscillations

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral and temporal changes that are characteristic of human language. These abilities lay the foundation for effective language acquisition; allowing infants to hone in on the sounds of their native language. Invasive procedures in animals and scalp-recorded potentials from human adults suggest that simultaneous, rhythmic activity (oscillations) between and within brain regions are fundamental to sensory development; determining the resolution with which incoming stimuli are parsed. At this time, little is known about oscillatory dynamics in human infant development. However, animal neurophysiology and adult EEG data provide the basis for a strong hypothesis that rapid auditory processing in infants is mediated by oscillatory synchrony in discrete frequency bands. In order to investigate this, 128-channel, high-density EEG responses of 4-month old infants to frequency change in tone pairs, presented in two rate conditions (Rapid: 70 msec ISI and Control: 300 msec ISI) were examined. To determine the frequency band and magnitude of activity, auditory evoked response averages were first co-registered with age-appropriate brain templates. Next, the principal components of the response were identified and localized using a two-dipole model of brain activity. Single-trial analysis of oscillatory power showed a robust index of frequency change processing in bursts of Theta band (3 - 8 Hz) activity in both right and left auditory cortices, with left activation more prominent in the Rapid condition. These methods have produced data that are not only some of the first reported evoked oscillations analyses in infants, but are also, importantly, the product of a well-established method of recording and analyzing clean, meticulously collected, infant EEG and ERPs. In this article, we describe our method for infant EEG net application, recording, dynamic brain response analysis, and representative results.

High-density electroencephalography (dEEG) is being used increasingly to study brain development and plasticity in the early years of life. Here we present an application of sophisticated analysis techniques that builds on traditional EEG recording to understand the oscillatory dynamics of rapid auditory processing in the infant brain.

Rapid auditory processing and acoustic change detection abilities play a critical role in allowing human infants to efficiently process the fine spectral ...

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., &lt;1 cm/&lt;1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning1, auditory processing2 and visual-spatial attention3. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function1-5. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user's intentions to enhance or improve communication6 and control7. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform8,9 and the SIGFRIED10 method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation.
Prerequisites and Planning:
Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient's brain, and on relevant factors of the patient's history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure.
In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient's performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital's institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient's endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort.
At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient's eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5th Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment.
Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software8,9. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator.
A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient's electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS).
Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping10.
The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

We present a method for collecting electrocorticographic signals for research purposes from humans who are undergoing invasive epilepsy monitoring. We show how to use the BCI2000 software platform for data collection, signal processing and stimulus presentation. Specifically, we demonstrate SIGFRIED, a BCI2000-based tool for real-time functional brain mapping.

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), ...

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Stereotactic Electroencephalography (SEEG) is a technique used to localize seizure foci in patients with medically intractable epilepsy. This procedure involves the chronic placement of multiple depth electrodes into regions of the brain typically inaccessible via subdural grid electrode placement. SEEG thus provides a unique opportunity to investigate brain function. In this paper we demonstrate how SEEG can be used to investigate the role of the dorsal anterior cingulate cortex (dACC) in cognitive control. We include a description of the SEEG procedure, demonstrating the surgical placement of the electrodes. We describe the components and process required to record local field potential (LFP) data from consenting subjects while they are engaged in a behavioral task. In the example provided, subjects play a cognitive interference task, and we demonstrate how signals are recorded and analyzed from electrodes in the dorsal anterior cingulate cortex, an area intimately involved in decision-making. We conclude with further suggestions of ways in which this method can be used for investigating human cognitive processes.

Stereotactic Electroencephalography (SEEG) is an operative technique used in epilepsy surgery to help localize seizure foci. It also affords a unique opportunity to investigate brain function. Here we describe how SEEG can be used to investigate cognitive processes in human subjects.

Stereotactic Electroencephalography (SEEG) is a technique used to localize seizure foci in patients with medically intractable epilepsy. This procedure ...

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen in one of two adjacent rooms. These rooms are separated by a glass window through which participants communicate to maintain feelings of togetherness while being fitted with the EEG cap. After checking for adequate EEG signals, the glass is covered by a curtain to prevent visual communication. Then, partners must be silent but are instructed to try to feel in the presence of their partner during the entire experiment. Just before it starts, participants are told that each of them will be presented with one image at a time and that these images will occur at the same time for both of them on their own screen. They are also instructed that, for each trial, the simultaneous images will always be different. However, unbeknownst to them, trials are randomized: only half of them are consistent with this instruction and actually include two different images. These trials form the DSC, that is, the different-stimuli condition. The other half of the trials are inconsistent with the instruction. They include two identical images and form the ISC (identical-stimuli condition). After the experiment, participants are sorted into two groups: those who reported that they felt in the presence of their partner during the majority of the trials and those who reported they did not. The impact of the stimulus processing of the partner is found by subtracting the mean voltages of the ERPs of the ISC (inconsistent with the instructions) from the ERPs of the DSC (consistent with the instructions) in at least two time windows (TWs): firstly, in the 75 to 150 ms TW, where the absolute values of these subtractions are greater, especially at right frontal sites, in those who felt in the presence of their partner than in those who did not; secondly, in the LPP time window (i.e., from 650 to 950 ms post onset), where ERPs are significantly less positive in the DSC than in the ISC in those in whom the raw results of the early (75-150ms) subtractions are negative.

This protocol describes key steps involved in assessing the sensitivity of the brain of one person to the stimulus processing of a close other by selecting pairs of partners, recording their electroencephalogram (EEG) simultaneously and computing their event-related brain potentials (ERPs).

The partners of each pair must be able to pass the McGill Friendship Questionnaire without communicating. Each partner is then seated in front of a screen ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, and less resource-intensive than imaging. Further, the functional data produced have excellent temporal resolution, which is not achievable with structural imaging.
Current applications of electroencephalograms (EEG) are limited by data processing methods. Standard analysis techniques using raw time series data at individual channels are very limited methods of interrogating nervous system activity. More detailed information about cortical function can be achieved by examining relationships between channels and deriving statistical models of how areas are interacting, allowing visualization of connectivity between networks.
This manuscript describes a method for deriving statistical models of cortical network activity by recording EEG in a standard manner, then examining the interelectrode coherence measures to assess relationships between the recorded areas. Higher order interactions can be further examined by assessing the covariance between the coherence pairs, producing high-dimensional &#34;maps&#34; of network interactions. These data constructs can be examined to assess cortical network function and its relationship to pathology in ways not achievable with traditional techniques.
This approach offers greater sensitivity to network level interactions than is achievable with raw time series analysis. It is, however, limited by the complexity of drawing specific mechanistic conclusions about the underlying neural populations and the high volumes of data generated, requiring more advanced statistical techniques for evaluation, including dimensionality reduction and classifier-based approaches.

Standard EEG analysis techniques offer limited insight into nervous system function. Deriving statistical models of cortical connectivity offers far greater ability to investigate underlying network dynamics. Improved functional assessment opens new possibilities for diagnosis, prognostication, and outcome prediction in nervous system diseases.

Non-invasive electrophysiological recordings are useful for the evaluation of nervous system function. These techniques are inexpensive, fast, replicable, ...

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Current web resources provide limited, user friendly tools to compute spectrograms for visualizing and quantifying electroencephalographic (EEG) data. This paper describes a Windows-based, open source code for creating EEG multitaper spectrograms. The compiled program is accessible to Windows users without software licensing. For Macintosh users, the program is limited to those with a MATLAB software license. The program is illustrated via EEG spectrograms that vary as a function of states of sleep and wakefulness, and opiate-induced alterations in those states. The EEGs of C57BL/6J mice were wirelessly recorded for 4 h after intraperitoneal injection of saline (vehicle control) and antinociceptive doses of morphine, buprenorphine, and fentanyl. Spectrograms showed that buprenorphine and morphine caused similar changes in EEG power at 1&#8722;3 Hz and 8&#8722;9 Hz. Spectrograms after administration of fentanyl revealed maximal average power bands at 3 Hz and 7 Hz. The spectrograms unmasked differential opiate effects on EEG frequency and power. These computer-based methods are generalizable across drug classes and can be readily modified to quantify and display a wide range of rhythmic biological signals.

This protocol provides an open source, compiled MATLAB program that generates multitaper spectrograms for electroencephalographic data.

Current web resources provide limited, user friendly tools to compute spectrograms for visualizing and quantifying electroencephalographic (EEG) data. ...

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG (good temporal resolution) and fMRI (good spatial resolution) to measure neuronal activity during an electrographic event, through hemodynamic responses known as blood-oxygen-level-dependent (BOLD) changes. It is a non-invasive research tool that is utilized in neuroscience research and is highly beneficial to the clinical community, especially for the management of neurological diseases, provided that proper equipment and protocols are administered during data acquisition. Although recording EEG-fMRI is apparently straightforward, the correct preparation, especially in placing and securing the electrodes, is not only important for safety but is also critical in ensuring the reliability and analyzability of the EEG data obtained. This is also the most experience-demanding part of the preparation. To address these issues, a straightforward protocol that ensures data quality was developed. This article provides a step-by-step guide for acquiring reliable EEG data during EEG-fMRI using this protocol that utilizes readily available medical products. The presented protocol can be adapted to different applications of EEG-fMRI in research and clinical settings, and may be beneficial to both inexperienced and expert operators.

This article provides a straightforward protocol for acquiring good quality electroencephalography (EEG) data during simultaneous EEG and functional magnetic resonance imaging by utilizing readily available medical products.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), EEG-fMRI, combines the complementary properties of scalp EEG ...

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two of the fundamental noninvasive methods for identifying brain activity. Multimodal methods have sought to combine the high temporal resolution of EEG with the spatial precision of fMRI, but the complexity of this approach is currently in need of improvement. The protocol presented here describes the recently developed spatiotemporal fMRI-constrained EEG source imaging method, which seeks to rectify source biases and improve EEG-fMRI source localization through the dynamic recruitment of fMRI sub-regions. The process begins with the collection of multimodal data from concurrent EEG and fMRI scans, the generation of 3D cortical models, and independent EEG and fMRI processing. The processed fMRI activation maps are then split into multiple priors, according to their location and surrounding area. These are taken as priors in a two-level hierarchical Bayesian algorithm for EEG source localization. For each window of interest (defined by the operator), specific segments of the fMRI activation map will be identified as active to optimize a parameter known as model evidence. These will be used as soft constraints on the identified cortical activity, increasing the specificity of the multimodal imaging method by reducing cross-talk and avoiding erroneous activity in other conditionally active fMRI regions. The method generates cortical maps of activity and time-courses, which may be taken as final results, or used as a basis for further analyses (analyses of correlation, causation, etc.) While the method is somewhat limited by its modalities (it will not find EEG-invisible sources), it is broadly compatible with most major processing software, and is suitable for most neuroimaging studies.

An EEG-fMRI multimodal imaging method, known as the spatiotemporal fMRI-constrained EEG source imaging method, is described here. The presented method employs conditionally-active fMRI sub-maps, or priors, to guide EEG source localization in a manner that improves spatial specificity and limits erroneous results.