This work supported in part by a NIH grant HL-65272. The authors thank Zachary T. Glovak and Clarence E. Locklear for their contributions to this project.

All procedures involving mice adhered to the Guide for the Care and Use of Laboratory Animals (8th edition, National Academies Press, Washington DC, 2011) and were reviewed and approved by the University of Tennessee Institutional Animal Care and Use Committee.
1. Implantation of Recording Electrodes and Initial Data Collection
<ol>
	<li>Purchase mice and keep them in a humidity- and temperature-controlled room with ad libitum access to food and water. Allow mice to adapt to their new environment for one week before surgical implantation of recording electrodes. The implantation procedure has been described in detail1,14.</li>
	<li>Sterilize all surgical equipment.</li>
	<li>Anesthetize mice with isoflurane at 2.5%&#8722;3% delivered in 100% oxygen.</li>
	<li>Following the loss of the righting reflex, remove the mouse from the anesthesia induction chamber and transfer it to a stereotaxic frame.</li>
	<li>Apply an ophthalmic ointment to both eyes.</li>
	<li>Reduce the isoflurane to 1.7%, continuously delivered via a mask.</li>
	<li>Make a midline scalp incision to expose the skull.</li>
	<li>Drill two craniotomies above the left and right cortex (each at stereotaxic coordinates anterior = 1.0 and lateral = 3.0 relative to the bregma15).</li>
	<li>Insert the EEG electrodes into each craniotomy and secure with dental acrylic.</li>
	<li>Implant bipolar electrodes in the dorsal trapezius muscle for recording the electromyogram (EMG). 
	NOTE: The four electrodes are led to a wireless telemeter implanted subcutaneously above the lower right body quadrant. These surgical techniques can be viewed here (https://www.datasci.com/services/dsi-surgical-services/surgical-videos).</li>
	<li>After the surgery, administer analgesic carprofen and place the mouse in a warm recovery cage. Observe the mouse until it is ambulatory. House implanted mice individually.</li>
	<li>Upon full recovery from the surgery, handle mice daily and evaluate the quality of the EEG and EMG recordings.</li>
	<li>Configure the data acquisition system to record all signals &#177; 1,000 mV.</li>
	<li>Obtain EEG and EMG recordings for the duration needed.</li>
	<li>Score each 10 s bin of the digital EEG and EMG recordings as wakefulness, rapid eye movement (REM) sleep, or non-REM (NREM) sleep using sleep scoring software. 
	NOTE: Among mouse strains there are genotype- and state-specific differences in EEG power expressed as a percentage of the total power16. In B6 mice, states of wakefulness are characterized by a 75&#8722;100 mV, mixed frequency EEG, and by EMG signals showing prominent muscle tone with large increases in amplitude during movement. Criteria for scoring NREM sleep include a reduction in EMG amplitude relative to the EMG amplitude of wakefulness. The NREM sleep EEG has a slower frequency and increased amplitude (100&#8722;150 mV) compared to wakefulness. REM sleep is characterized by muscle atonia and an EEG signal that is similar to the EEG of wakefulness.</li>
	<li>Instruct two individuals to independently score the same record. At least one individual should be blinded to the treatment condition. Concordance values between the two sleep scorers should be greater than 90%.</li>
</ol>
2. Facilities and Equipment
<ol>
	<li>Amplify and digitize unfiltered EEG and EMG signals using data acquisition instrumentation and software. 
	NOTE: The Chronux Spectral Analysis Toolbox developed in the Mitra Laboratory at Cold Spring Harbor Laboratory is used to express EEG signals as power in relation to time and frequency domains.</li>
</ol>
3. Spectrogram Computation
<ol>
	<li>If a Windows user, use the compiled program.</li>
	<li>If a Macintosh user, run the raw code file.</li>
	<li>Obtain raw, unprocessed EEG data in either EDF or CSV file format and place it in the same location as the compiled program file.
	<ol>
		<li>Name the data files using the following constraints: names must consist of only letters, numbers, underscores, or dashes.</li>
		<li>Name the data files using the following constraints: filenames must not contain periods, commas, spaces, or any other symbols.</li>
	</ol>
	</li>
	<li>Download the compiled Multitaper Spectrogram Program (<a href="https://drive.google.com/drive/folders/1cyb3T3IkjQvkc-H6lm__17CpRMqkTbNt?usp=sharing" target="_blank">https://drive.google.com/</a> or contact C. O'Brien at <a href="mailto:cobrien8@vols.utk.edu">cobrien8@vols.utk.edu</a>).</li>
	<li>Launch the spectrogram program and follow the pop-up prompts. Choose file type: *.CSV or *.EDF. 
	NOTE: Further program installation details are located in the readme.txt file.</li>
	<li>Type the entire EEG file name (e.g., 419eeg.edf or 419.eeg.csv).</li>
	<li>Select parameters for spectrogram calculation: Default or New. This step requires the longest processing time as the spectrogram is being computed. The mathematical windowing function (taper) provides statistically-independent estimates of the underlying spectrum. The longer the recording duration, the longer this step will take. On a PC platform running Windows 10 this required a maximum of about 3&#8722;4 min for a 4 h recording.
	<ol>
		<li>Use the following default spectrogram parameters: 
		Sampling frequency = 500 Hz. This represents the number of samples per second. 
		fpass = 0.3 Hz and 30 Hz. Fpass defines the frequencies of input and controls the range of frequencies supplied in the output. 
		Padding = 2. Padding works to finely interpolate the output without affecting the result calculation in any way. This may assist with visualization and the precise identification of spectral lines. The field is any integer from -1 and up. 
		Time-bandwidth product (NW) = 15. The product of the signal temporal duration and spectral width. 
		Number of tapers = 29. When choosing number of tapers, it is essential to use 2NW-1. There is no limit on the number of tapers used. The more tapers used will result in the inclusion of tapers with poor concentration in the specified frequency bandwidth. 
		Trial average = 1. This parameter governs whether or not trial or channel averaging is performed. If this parameter is set to 0, there is no channel averaging and the function will output independent results for each trial or channel passed as input data. However, if the trial average is set to 1, the results output to the user are averaged over trials or channels. 
		Time to compute FFT ~30 s. Used to follow the evolution of the spectrum by calculating the spectrum over many small windows. 
		Step size of window for FFT computation = 5. The amount the sliding time window advances after each spectrum calculation is performed. 
		NOTE: The default spectrogram parameters stated in step 3.7.1 can be changed as needed.</li>
	</ol>
	</li>
	<li>Enter titles for both the spectrogram and EEG.</li>
	<li>Save the resulting spectrogram and EEG.
	<ol>
		<li>Save figures by clicking File | Save in the figure window. 
		NOTE: The figures will provide program users with summaries that can be developed into publication-quality figures.</li>
	</ol>
	</li>
</ol>
4. Troubleshooting
<ol>
	<li>Download the sample mouse sleep EEG data for sample spectrogram computation.</li>
	<li>Run program with the sample data to ensure the user is correctly using the program. Find the figures for these sample data in the appendix to ensure that the figures created from the sample data are accurate. 
	NOTE: All equipment and materials used have been provided in the Table of Materials.</li>
</ol>

<ol>
	<li>Wasilczuk, A. Z., Proekt, A., Kelz, M. B., McKinstry-Wu, 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=High-density+electroencephalographic+acquisition+in+a+rodent+model+using+low-cost+and+open+source+resources.">High-density electroencephalographic acquisition in a rodent model using low-cost and open source resources.</a> Journal of Visualized Experiments. 117, 10 (2016).</li><li>Prerau, M. J., Brown, R. E., Bianchi, M. T., Ellenbogen, J. M., Purdon, P. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep+neurophysiological+dynamics+through+the+lens+of+multitaper+spectral+analysis.">Sleep neurophysiological dynamics through the lens of multitaper spectral analysis.</a> Physiology. 32, 60-92 (2017).</li><li>Akin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+wavelet+transform+and+FFT+methods+in+the+analysis+of+EEG+signals.">Comparison of wavelet transform and FFT methods in the analysis of EEG signals.</a> Journal of Medical Systems. 26 (3), 241-247 (2002).</li><li>Purdon, P. L., Sampson, A., Pavone, A., Brown, E. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clinical+electroencephalography+for+anesthesiologists+Part+1:+Background+and+basic+signatures.">Clinical electroencephalography for anesthesiologists Part 1: Background and basic signatures.</a> Anesthesiology. 123 (4), 937-960 (2015).</li><li>Liu, Q., 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=Frontal+EEG+temporal+and+spectral+dynamics+similarity+analysis+between+propofol+and+desflurane+induced+anesthesia+using+Hilbert-Huang+Transform.">Frontal EEG temporal and spectral dynamics similarity analysis between propofol and desflurane induced anesthesia using Hilbert-Huang Transform.</a> BioMed Research International. 2018, 4939480 (2018).</li><li>Akeju, O., 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=Spatiotemporal+dynamics+of+dexmedetomidine-induced+electroencephalogram+oscillations.">Spatiotemporal dynamics of dexmedetomidine-induced electroencephalogram oscillations.</a> PLoS One. 11 (10), (2016).</li><li>Ogilvie, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+process+of+falling+asleep.">The process of falling asleep.</a> Sleep Medicine Reviews. 5 (3), 247-270 (2001).</li><li>Baghdoyan, H. A., Lydic, R., Brady, S. T., Albers, R. W., Price, D. L., Siegel, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Basic Neurochemistry. , 982-999 (2012).</li><li>Angel, C., 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=Buprenorphine+depresses+respiratory+variablity+in+obese+mice+with+altered+leptin+signaling.">Buprenorphine depresses respiratory variablity in obese mice with altered leptin signaling.</a> Anesthesiology. 128 (5), 984-991 (2018).</li><li>Glovak, Z. T., Mihalko, S., Baghdoyan, H. A., Lydic, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Leptin+status+alters+buprenorphine-induced+antinociception+in+obese+mice+with+dysfunctional+leptin+receptors.">Leptin status alters buprenorphine-induced antinociception in obese mice with dysfunctional leptin receptors.</a> Neuroscience Letters. 660, 29-33 (2017).</li><li>Zhang, X., 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=Morphine+and+fentanyl+delivered+to+prefrontal+cortex+of+behaving+mice+depress+breathing+and+alter+neurotransmitter+concentrations.">Morphine and fentanyl delivered to prefrontal cortex of behaving mice depress breathing and alter neurotransmitter concentrations.</a> Anesthesia &amp; Analgesia. , (2019).</li><li>Cornelissen, L., Kim, S. E., Purdon, P. L., Brown, E. N., Berde, C. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age-dependent+electroencephalogram+(EEG)+patterns+during+sevoflurane+general+anesthesia+in+infants.">Age-dependent electroencephalogram (EEG) patterns during sevoflurane general anesthesia in infants.</a> eLIFE. 4, e06513 (2015).</li><li>Chini, M., 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=Neural+correlates+of+anesthesia+in+newborn+mice+and+humans.">Neural correlates of anesthesia in newborn mice and humans.</a> Front Neural Circuits. 13 (Article 38), 1-13 (2019).</li><li>Flint, R. R., Chang, T., Lydic, R., Baghdoyan, H. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GABA-A+receptors+in+the+pontine+reticular+formation+of+C57BL/6J+mouse+modulate+neurochemical,+electrographic,+and+behavioral+phenotypes+of+wakefulness.">GABA-A receptors in the pontine reticular formation of C57BL/6J mouse modulate neurochemical, electrographic, and behavioral phenotypes of wakefulness.</a> Journal of Neuroscience. 30 (37), 12301-12309 (2010).</li><li>Paxinos, G., Franklin, K. B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Mouse Brain in Stereotaxic Coordinates. , (2018).</li><li>Franken, P., Malafosse, A., Tafti, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+variation+in+EEG+activity+during+sleep+in+inbred+mice.">Genetic variation in EEG activity during sleep in inbred mice.</a> American Journal of Physiology. 257 (4), (1998).</li><li>Ko, C. H., 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=Emergence+of+noise-induced+oscillations+in+the+central+circadian+pacemaker.">Emergence of noise-induced oscillations in the central circadian pacemaker.</a> PLoS Biol. 8 (10), e1000513 (2010).</li><li>Steriade, M., Curr&#243; Dossi, R., Conteras, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrophysiological+properties+of+intralaminar+thalamocortical+cells+discharging+rhythmic+(40+Hz)+spike-bursts+at+1000+Hz+during+waking+and+rapid+eye+movement+sleep.">Electrophysiological properties of intralaminar thalamocortical cells discharging rhythmic (40 Hz) spike-bursts at 1000 Hz during waking and rapid eye movement sleep.</a> Neuroscience. 56, 1-9 (1993).</li></ol>

The authors have no conflicts of interest.

The program described here was developed to create a spectrogram using the nine steps outlined in protocol section 3, Spectrogram Computation. These steps involve acquiring the spectrogram program, ensuring the correct file format, and altering the computational parameters for the generation of unique user spectrograms. Users can create spectrograms that are tailored to a range of conceptual questions and experimental designs. In order to enhance the ease and efficiency of this development process, it is essential to provide the raw EEG data in the correct file format, named according to the constraints described above. Although example signals have been provided for mouse EEG data, the spectrogram program is readily applicable to human and non-human EEG data that are free of signal processing limitations.
The recommended approach for troubleshooting and method modification is to begin by analyzing a small data set. The major program outputs to consider include plots of the filtered EEG as well as the spectrogram. An appealing aspect of the tapered spectrogram is that it can be applied to a wide variety of periodic, biological signals. The variety ranges from long duration circadian (24 h) rhythms17 to very fast rhythms such as 1,000 Hz discharge rates of a Renshaw cell18.
Data formatting is a constraint of this spectrogram protocol. European data format (EDF) is widely used with EEG data. However, there are many other formatting options. For this reason, the raw code file has been included (see 3.2 above) in case the user would like to alter the file format. With regard to the raw program file, another limitation is the need for experience with the computer programming language in order to alter the file format. Not all investigators have access to the proprietary software and the full array of plug-ins. This protocol was developed to circumvent this issue by providing a compiled program that runs on a WINDOWS-based device without software licensing. This is achieved through the RUNTIME plugin which is included in the compiled program and does not require any software registration by the user.
This EEG spectrogram routine is a novel, open source, computer-based program that allows users to create personalized, multitaper spectrograms from a wide range of data. The user has complete control over all computational aspects of spectrogram generation. Without prior signal processing and computer programing knowledge, spectrograms can be difficult to generate. The protocol described here will facilitate spectrogram generation. Please see the supplemental material section for further signal processing readings and multitaper spectrogram guidance.
Supplemental Material 
<a href="http://chronux.org" target="_blank">http://chronux.org</a> 
<a href="http://www-users.med.cornell.edu/~jdvicto/pdfs/pubo08.pdf" target="_blank">http://www-users.med.cornell.edu/~jdvicto/pdfs/pubo08.pdf</a> 
<a href="http://www.fieldtriptoolbox.org/tutorial/timefrequencyanalysis/" target="_blank">http://www.fieldtriptoolbox.org/tutorial/timefrequencyanalysis/</a> 
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4502759/#SD3-data" target="_blank">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4502759/#SD3-data</a>

EEG data can be productively analyzed in the frequency domain to characterize levels of behavioral and neurophysiological arousal1. Multitaper spectrograms transform the EEG waveform into time and frequency domains, resulting in the visualization of the dynamic signal power at different frequencies across time. The multitaper spectrogram uses Fourier analysis to produce spectral density estimations. Spectral density estimation separates a waveform into the pure sinusoidal waves comprising the signal and is analogous to the diffraction of white light through a prism to see the entire spectrum of colors2. The multitaper spectrogram of the EEG represents the combined activity of multiple networks of neurons with discharge patterns that oscillate at different frequencies2. Due to its time shift invariant, the Fourier transform is considered the best transformation between time and frequency domains3. Fourier analysis also has a number of limitations. EEG signals are nonstationary. Therefore, small changes may not be perceived under Fourier methods and the analysis may change depending on the size of the data set. However, windowing is used when applying a Fourier transform to a nonstationary signal. This assumes that the spectrum of the signal changes only marginally over short periods of time. An alternate method for spectral analysis is wavelet transform which may be more appropriate for detecting brain disease3.
From a functional perspective, the different oscillations comprising an EEG signal are lower-level, trait phenotypes characteristic of higher-level, state phenotypes such as sleep and wakefulness2, or the loss of wakefulness caused by general anesthetics4,5,6. Regarding states of sleep and wakefulness, the spectrogram clearly illustrates that endogenously generated rhythms of sleep are continuous and dynamic7. Quantitative descriptions of states of sleep and wakefulness have traditionally involved a binning process that assigns a sleep or wake classification to each specifically defined epoch (e.g., 10 s) of EEG recording. These state bins are then plotted as a function of time. Time course data plots, often referred to as hypnograms, are used to differentiate normal sleep from sleep that is disrupted by disease, drug administration, changes in circadian rhythms, shift work, etc. A limitation of hypnogram plots is that they misrepresent EEG signals by expressing arousal states as square waveforms. Hypnogram plotting involves a discretization of arousal states2 and does not permit a finely grained display of intermediate or transition stages. Furthermore, 10 s scoring epochs produce a discretization of time by imposing a lower limit on the time scale. The result of the discretization of both state and time is the loss of neurophysiological information regarding the dynamic interplay between states of consciousness2 and drug-induced disruption of these states4. For example, different anesthetic agents act on different molecular targets and neural networks. Pharmacological manipulation of these neural networks reliably produces spectrograms unique to the drug, dose, and route of administration4.
The present protocol was developed to facilitate research regarding the mechanisms by which opioids alter sleep8, breathing9, nociception10, and brain neurochemistry11. This protocol describes the steps required to create a multitapered spectrogram for EEG analyses that can be completed using proprietary software or a system that does not have MATLAB licensing. C57BL/6J (B6) mice were used to validate the ability of this computer-based method to create novel EEG spectrograms during normal, undisturbed states of sleep and wakefulness and after systemic administration of opiates. The reliability and validity of the analyses were confirmed by systematic comparisons of differences between EEG spectrograms after B6 mice received intraperitoneal injections of saline (vehicle control) and antinociceptive doses of morphine, buprenorphine, and fentanyl.
Quantitative studies of neonatal mouse EEG dynamics have translational relevance by providing a model for studies aiming to achieve a better understanding of neonatal human EEG12. Quantifying EEG dynamics is not merely descriptive and can contribute to machine learning approaches that can predict arousal based in part on EEG data13. The goal of the present report is to promote translational science by providing a widely accessible, user friendly code for computing multitaper spectrograms that characterize drug-induced changes in mouse EEG.

<table><tbody><tr><td>Dental acrylic</td><td>Lang Dental Manufacturing Co</td><td>Jet powder and liquid</td><td></td></tr><tr><td>EEG/EMG Amplifier</td><td>Data Science International</td><td>model MX2</td><td></td></tr><tr><td>macOS Mojave</td><td>Apple</td><td>v10.14.4</td><td></td></tr><tr><td>MATLAB</td><td>Mathworks</td><td>v9.4.0.813654</td><td>software for spectrogram comp.</td></tr><tr><td>Mouse anesthesia mask</td><td>David Kopf Instruments</td><td>model 907</td><td></td></tr><tr><td>Neuroscore</td><td>Data Science International</td><td>v3.3.9317-1</td><td>software for scoring sleep and wakefulness</td></tr><tr><td>Ponemah</td><td>Data Science International</td><td>v5.32</td><td>software for EEG/EMG Data Acquisition</td></tr><tr><td>Stereotaxic frame</td><td>David Kopf Instruments</td><td>model 962</td><td></td></tr><tr><td>Stereotaxic frame, mouse adapter</td><td>David Kopf Instruments</td><td>model 921</td><td></td></tr><tr><td>Windows 10</td><td>Microsoft</td><td>v10.0.17763.503</td><td></td></tr><tr><td>Wireless Telemeter</td><td>Data Science International</td><td>model HD-X02</td><td></td></tr></tbody></table>

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.

The following figures illustrate the type of novel insights into EEG indices of brain excitability that are provided by spectrograms.
Figure 1A illustrates similarities and differences in the cortical EEG during wakefulness, NREM sleep, and REM sleep. Many investigators use these kinds of traces, along with EMG recordings (not shown), to quantify sleep and wakefulness. Figure 1B uses a hypnogram plot to convey the temporal organization of states of sleep and wakefulness based on assessments of EEG and EMG recordings. States were scored in 10 s epochs and these epochs were plotted as the hypnogram during the 14,400 s comprising the 4 h recording. Hypnogram plots do not illustrate the fact that transitions between states are continuous and nonlinear. In contrast to a hypnogram plot, the spectrogram (Figure 1C) illustrates highly dynamic changes in EEG frequency and power as a function of time. The spectrogram also highlights the similarities between the cortical EEG signal during wakefulness and during REM sleep. The three boxes superimposed on the spectrogram (Figure 1C) mark states identified as wakefulness (WAKE), NREM sleep, and REM sleep in the hypnogram above (Figure 1B) and are provided to assist with visualizing the detailed changes in EEG frequency and power. The spectrogram for the entire recording provides a nuanced appreciation of the EEG as a continuous process.
Figure 2 provides four multitaper spectrograms, each summarizing 4 h of EEG recordings after intraperitoneal administration of saline, morphine, buprenorphine, and fentanyl. All four recordings are from the same mouse and were begun 2 h after light onset. The opiates, but not saline, inhibited NREM and REM sleep, and increased the amount of wakefulness. A number of novel features are visualized by the spectrograms. The detection of novel EEG features suggests the potential application for opiate differentiation in a chemical threat environment. After the saline injection (Figure 2A) the greatest amount of power resided in the 2&#8722;4 Hz range, indicating NREM sleep. Note that the EEG spectrograms were fundamentally altered by opiate administration, and that each opiate caused unique spectral changes.
Figure 3 demonstrates that EEG changes illustrated by the spectrograms can be quantified and expressed as average dominate spectral power of each half frequency (Figure 3A) and as the average spectral power within specific EEG frequency bands (Figure 3B). The greatest differences from saline were caused by buprenorphine and occurred in the delta and theta ranges.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60333/60333fig01.jpg" /> 
Figure 1: Cortical EEG recordings used to create hypnograms and spectrograms. (A) EEG waveforms recorded during wakefulness, NREM sleep, and REM sleep during a baseline (no injection) recording. Each trace shows 90 s of recording. (B) The hypnogram uses the height of the bars to convey state of consciousness (ordinate) versus 4 h of recording (abscissa). (C) Tapered spectrogram using a color bar to convey EEG power in decibels (dB, right ordinate) or spectral power density at different EEG frequencies in Hertz (Hz, left ordinate) as a function of 4 h recording time (abscissa). Black vertical lines have been added to the spectrogram to delineate one episode each of wakefulness, NREM sleep, and REM sleep. (Spectrogram parameters: sampling frequency = 500 Hz, fpass = 0.3 Hz and 30 Hz, padding = 2, time-bandwidth = 15, number of tapers 29, trial average = 1, duration of time to compute FFT ~30 s, step size of window for FFT computation = 5). <a href="https://www.jove.com/files/ftp_upload/60333/60333fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60333/60333fig02.jpg" /> 
Figure 2: Spectrograms illustrating changes in EEG power and frequency caused by opiate administration. Each spectrogram plots EEG frequency in Hertz (Hz, left ordinate) and decibels (dB) of EEG power using a color bar (right ordinate) for 4 h (abscissa) after administration of (A) saline, (B) morphine, (C) buprenorphine, and (D) fentanyl. (Spectrogram parameters: sampling frequency = 500 Hz, fpass = 0.3 Hz and 30 Hz, padding = 2, time-bandwidth = 15, number of tapers 29, trial average = 1, time to compute FFT ~30 s, step size of window for FFT computation = 5). <a href="https://www.jove.com/files/ftp_upload/60333/60333fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60333/60333fig03.jpg" /> 
Figure 3: Opiates differentially altered the average EEG power within the delta and theta EEG frequency bands. (A) Summarizes the average EEG power during each 4 h recording shown in Figure 2. Ordinate plots average EEG power at each half frequency (abscissa). Relative to the saline control, each of the other three functions show opiate-specific alterations in average EEG power. (B) Illustrates the average EEG power in four EEG frequency bands (delta, theta, alpha, and beta) after administration of saline (S), buprenorphine (B), morphine (M), and fentanyl (F). Color coding is the same for power functions in A and average power bands in B. <a href="https://www.jove.com/files/ftp_upload/60333/60333fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Computer-based Multitaper Spectrogram Program for Electroencephalographic Data at JoVE.com

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

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. ...

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Research

JoVE Journal

Neuroscience

12.0K Views.  University of Tennessee. This protocol provides an open source, compiled MATLAB program that generates multitaper spectrograms for electroencephalographic data.

Video: Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Cortical Source Analysis of High-Density EEG Recordings in Children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.&#160;
In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how&#160;EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.&#160;

In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and ...

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 ...

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

In epilepsy, seizures can evoke cardiac rhythm disturbances such as heart rate changes, conduction blocks, asystoles, and arrhythmias, which can potentially increase risk of sudden unexpected death in epilepsy (SUDEP). Electroencephalography (EEG) and electrocardiography (ECG) are widely used clinical diagnostic tools to monitor for abnormal brain and cardiac rhythms in patients. Here, a technique to simultaneously record video, EEG, and ECG in mice to measure behavior, brain, and cardiac activities, respectively, is described. The technique described herein utilizes a tethered (i.e., wired) recording configuration in which the implanted electrode on the head of the mouse is hard-wired to the recording equipment. Compared to wireless telemetry recording systems, the tethered arrangement possesses several technical advantages such as a greater possible number of channels for recording EEG or other biopotentials; lower electrode costs; and greater frequency bandwidth (i.e., sampling rate) of recordings. The basics of this technique can also be easily modified to accommodate recording other biosignals, such as electromyography (EMG) or plethysmography for assessment of muscle and respiratory activity, respectively. In addition to describing how to perform the EEG-ECG recordings, we also detail methods to quantify the resulting data for seizures, EEG spectral power, cardiac function, and heart rate variability, which we demonstrate in an example experiment using a mouse with epilepsy due to Kcna1 gene deletion. Video-EEG-ECG monitoring in mouse models of epilepsy or other neurological disease provides a powerful tool to identify dysfunction at the level of the brain, heart, or brain-heart interactions.

Here, we present a protocol to record brain and heart bio signals in mice using simultaneous video, electroencephalography (EEG), and electrocardiography (ECG). We also describe methods to analyze the resulting EEG-ECG recordings for seizures, EEG spectral power, cardiac function, and heart rate variability.

In epilepsy, seizures can evoke cardiac rhythm disturbances such as heart rate changes, conduction blocks, asystoles, and arrhythmias, which can ...

Genetics

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables comparative calculation of individuals&#39; neural efficiency as they perform successive tasks requiring spatial ability to derive solutions. Neural efficiency here is defined as having less beta activation, and therefore expending fewer neural resources, to perform a task in comparison to other groups or other tasks. For inter-task comparisons of tasks with similar durations, these measurements may enable a comparison of task type difficulty. For intra-participant and inter-participant comparisons, these measurements provide potential insight into the participant&#39;s level of spatial ability and different engineering problem solving tasks. Performance on the selected tasks can be analyzed and correlated with beta activities. This work presents a detailed research protocol studying the neural efficiency of students engaged in the solving of typical spatial ability and Statics problems. Students completed problems specific to the Mental Cutting Test (MCT), Purdue Spatial Visualization test of Rotations (PSVT:R), and Statics. While engaged in solving these problems, participants&#39; brain waves were measured with EEG allowing data to be collected regarding alpha and beta brain wave activation and use. The work looks to correlate functional performance on pure spatial tasks with spatially intensive engineering tasks to identify the pathways to successful performance in engineering and the resulting improvements in engineering education that may follow.

This manuscript describes an approach to measure neural activity of humans while solving spatially focused engineering problems. The electroencephalogram methodology helps interpret beta brain wave measurements in terms of neural efficiency, with the aim of ultimately enabling comparisons of task performance both between problem types and between participants.

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables ...

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.

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 ...

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 ...

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations over time in the blood-oxygenation level dependent signal. rs-fcMRI has been applied extensively to identify abnormalities in brain connectivity in different neurologic and psychiatric diseases. However, the relationship among rs-fcMRI connectivity abnormalities, brain electrophysiology and disease state is unknown, in part because the causal significance of alterations in functional connectivity in disease pathophysiology has not been established. Transcranial Magnetic Stimulation (TMS) is a technique that uses electromagnetic induction to noninvasively produce focal changes in cortical activity. When combined with electroencephalography (EEG), TMS can be used to assess the brain's response to external perturbations. Here we provide a protocol for combining rs-fcMRI, TMS and EEG to assess the physiologic significance of alterations in functional connectivity in patients with neuropsychiatric disease. We provide representative results from a previously published study in which rs-fcMRI was used to identify regions with abnormal connectivity in patients with epilepsy due to a malformation of cortical development, periventricular nodular heterotopia (PNH). Stimulation in patients with epilepsy resulted in abnormal TMS-evoked EEG activity relative to stimulation of the same sites in matched healthy control patients, with an abnormal increase in the late component of the TMS-evoked potential, consistent with cortical hyperexcitability. This abnormality was specific to regions with abnormal resting-state functional connectivity. Electrical source analysis in a subject with previously recorded seizures demonstrated that the origin of the abnormal TMS-evoked activity co-localized with the seizure-onset zone, suggesting the presence of an epileptogenic circuit. These results demonstrate how rs-fcMRI, TMS and EEG can be utilized together to identify and understand the physiological significance of abnormal brain connectivity in human diseases.

Resting-state functional-connectivity MRI has identified abnormalities in patients with a wide range of neuropsychiatric disorders, including epilepsy due to malformations of cortical development. Transcranial Magnetic Stimulation in combination with EEG can demonstrate that patients with epilepsy have cortical hyperexcitability in regions with abnormal connectivity.

Resting-state functional connectivity MRI (rs-fcMRI) is a technique that identifies connectivity between different brain regions based on correlations ...

Medicine

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data and have been widely used to investigate the neural mechanisms in some brain disorders. The goal of this article is to describe the protocol underlying EEG microstate and omega complexity analyses step by step. The main advantage of these two measures is that they could eliminate the reference-dependent problem inherent to traditional spectrum analysis. In addition, microstate analysis makes good use of high time resolution of resting-state EEG, and the four obtained microstate classes could match the corresponding resting-state networks respectively. The omega complexity characterizes the spatial complexity of the whole brain or specific brain regions, which has obvious advantage compared with traditional complexity measures focusing on the signal complexity in a single channel. These two EEG measures could complement each other to investigate the brain complexity from the temporal and spatial domain respectively.

This article describes the protocol underlying electroencephalography (EEG) microstate analysis and omega complexity analysis, which are two reference-free EEG measures and highly valuable to explore the neural mechanisms of brain disorders.

Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of ...

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 ...

Unveiling Brain-Heart Interplay with EEG and Cardio Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

The BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It includes heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. The protocol will aid in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human ...