Scott Makeig

YNIMG Journal 2022 Journal Article

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG

• Sheng-Hsiou Hsu
• Yayu Lin
• Julie Onton
• Tzyy-Ping Jung
• Scott Makeig

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1-2 h), high-density (128-channel) EEG data recorded while participants used guided imagination to imagine situations stimulating the experience of 15 specified emotions. These decompositions tended to return models identifying spatiotemporal EEG patterns or states within single emotion imagination periods. Model probability transitions reflected time-courses of EEG dynamics during emotion imagination, which varied across emotions. Transitions between models accounting for imagined "grief" and "happiness" were more abrupt and better aligned with participant reports, while transitions for imagined "contentment" extended into adjoining "relaxation" periods. The spatial distributions of brain-localizable independent component processes (ICs) were more similar within participants (across emotions) than emotions (across participants). Across participants, brain regions with differences in IC spatial distributions (i.e., dipole density) between emotion imagination versus relaxation were identified in or near the left rostrolateral prefrontal, posterior cingulate cortex, right insula, bilateral sensorimotor, premotor, and associative visual cortex. No difference in dipole density was found between positive versus negative emotions. AMICA models of changes in high-density EEG dynamics may allow data-driven insights into brain dynamics during emotional experience, possibly enabling the improved performance of EEG-based emotion decoding and advancing our understanding of emotion.

YNIMG Journal 2021 Journal Article

Capturing the nature of events and event context using hierarchical event descriptors (HED)

• Kay Robbins
• Dung Truong
• Stefan Appelhoff
• Arnaud Delorme
• Scott Makeig

Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities including fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and reproducibility of neuroimaging datasets for further analysis and meta- or mega-analysis. A powerful annotation strategy using the new third-generation formulation of the Hierarchical Event Descriptors (HED) framework and tools (hedtags.org) combines robust event description with details of experiment design and metadata in a human-readable as well as machine-actionable form, making event annotation relevant to the full range of neuroimaging and other time series data. This paper considers the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson made available by its authors as a Brain Imaging Data Structure (BIDS) dataset (bids.neuroimaging.io). We propose a set of best practices and guidelines for event annotation integrated in a natural way into the BIDS metadata file architecture, examine the impact of event design decisions, and provide a working example of organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using HED can document events occurring during neuroimaging experiments as well as their interrelationships, providing machine-actionable annotation enabling automated within- and across-experiment analysis and comparisons. We discuss the evolution of HED software tools and have made available an accompanying HED-annotated BIDS-formated edition of the MEEG data of the Wakeman and Henson dataset (openneuro.org, ds003645).

YNIMG Journal 2021 Journal Article

The open EEGLAB portal Interface: High-Performance computing with EEGLAB

• Ramón Martínez-Cancino
• Arnaud Delorme
• Dung Truong
• Fiorenzo Artoni
• Kenneth Kreutz-Delgado
• Subhashini Sivagnanam
• Kenneth Yoshimoto
• Amitava Majumdar

EEGLAB signal processing environment is currently the leading open-source software for processing electroencephalographic (EEG) data. The Neuroscience Gateway (NSG, nsgportal.org) is a web and API-based portal allowing users to easily run a variety of neuroscience-related software on high-performance computing (HPC) resources in the U.S. XSEDE network. We have reported recently (Delorme et al., 2019) on the Open EEGLAB Portal expansion of the free NSG services to allow the neuroscience community to build and run MATLAB pipelines using the EEGLAB tool environment. We are now releasing an EEGLAB plug-in, nsgportal, that interfaces EEGLAB with NSG directly from within EEGLAB running on MATLAB on any personal lab computer. The plug-in features a flexible MATLAB graphical user interface (GUI) that allows users to easily submit, interact with, and manage NSG jobs, and to retrieve and examine their results. Command line nsgportal tools supporting these GUI functionalities allow EEGLAB users and plug-in tool developers to build largely automated functions and workflows that include optional NSG job submission and processing. Here we present details on nsgportal implementation and documentation, provide user tutorials on example applications, and show sample test results comparing computation times using HPC versus laptop processing.

YNIMG Journal 2019 Journal Article

ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

• Luca Pion-Tonachini
• Ken Kreutz-Delgado
• Scott Makeig

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low-cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) the ICLabel dataset containing spatiotemporal measures for over 200, 000 ICs from more than 6000 EEG recordings and matching component labels for over 6000 of those ICs, all using common average reference, (2) the ICLabel website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier, freely available for MATLAB. The ICLabel classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The classifier outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories while computing those labels ten times faster than that classifier as shown by a systematic comparison against other publicly available EEG IC classifiers.

YNIMG Journal 2019 Journal Article

Measuring transient phase-amplitude coupling using local mutual information

• Ramón Martínez-Cancino
• Joseph Heng
• Arnaud Delorme
• Ken Kreutz-Delgado
• Roberto C. Sotero
• Scott Makeig

Here we demonstrate the suitability of a local mutual information measure for estimating the temporal dynamics of cross-frequency coupling (CFC) in brain electrophysiological signals. In CFC, concurrent activity streams in different frequency ranges interact and transiently couple. A particular form of CFC, phase-amplitude coupling (PAC), has raised interest given the growing amount of evidence of its possible role in healthy and pathological brain information processing. Although several methods have been proposed for PAC estimation, only a few have addressed the estimation of the temporal evolution of PAC, and these typically require a large number of experimental trials to return a reliable estimate. Here we explore the use of mutual information to estimate a PAC measure (MIPAC) in both continuous and event-related multi-trial data. To validate these two applications of the proposed method, we first apply it to a set of simulated phase-amplitude modulated signals and show that MIPAC can successfully recover the temporal dynamics of the simulated coupling in either continuous or multi-trial data. Finally, to explore the use of MIPAC to analyze data from human event-related paradigms, we apply it to an actual event-related human electrocorticographic (ECoG) data set that exhibits strong PAC, demonstrating that the MIPAC estimator can be used to successfully characterize amplitude-modulation dynamics in electrophysiological data.

YNICL Journal 2019 Journal Article

Neural activation and connectivity during cued eye blinks in Chronic Tic Disorders

• Sandra K. Loo
• Makoto Miyakoshi
• Kelly Tung
• Evan Lloyd
• Giulia Salgari
• Andrea Dillon
• Susanna Chang
• John Piacentini

OBJECTIVE: The pathophysiology of Chronic Tic Disorders (CTDs), including Tourette Syndrome, remains poorly understood. The goal of this study was to compare neural activity and connectivity during a voluntary movement (VM) paradigm that involved cued eye blinks among children with and without CTDs. Using the precise temporal resolution of electroencephalography (EEG), we used the timing and location of cortical source resolved spectral power activation and connectivity to map component processes such as visual attention, cue detection, blink regulation and response monitoring. We hypothesized that neural activation and connectivity during the cued eye blink paradigm would be significantly different in regions typically associated with effortful control of eye blinks, such as frontal, premotor, parietal, and occipital cortices between children with and without CTD. METHOD: Participants were 40 children (23 with CTD, 17 age-matched Healthy Control [HC]), between the ages of 8-12 (mean age = 9.5) years old. All participants underwent phenotypic assessment including diagnostic interviews, behavior rating scales and 128-channel EEG recording. Upon presentation of a cue every 3 s, children were instructed to make an exaggerated blink. RESULTS: Behaviorally, the groups did not differ in blink number, latency, or ERP amplitude. Within source resolved clusters located in left dorsolateral prefrontal cortex, posterior cingulate, and supplemental motor area, children with CTD exhibited higher gamma band spectral power relative to controls. In addition, significant diagnostic group differences in theta, alpha, and beta band power in inferior parietal cortex emerged. Spectral power differences were significantly associated with clinical characteristics such as tic severity and premonitory urge strength. After calculating dipole density for 76 anatomical regions, the CTD and HC groups had 70% overlap of top regions with the highest dipole density, suggesting that similar cortical networks were used across groups to carry out the VM. The CTD group exhibited significant information flow increase and dysregulation relative to the HC group, particularly from occipital to frontal regions. CONCLUSION: Children with CTD exhibit abnormally high levels of neural activation and dysregulated connectivity among networks used for regulation and effortful control of voluntary eye blinks.

YNIMG Journal 2019 Journal Article

Trial-by-trial source-resolved EEG responses to gait task challenges predict subsequent step adaptation

• Johanna Wagner
• Ramón Martínez-Cancino
• Scott Makeig

A growing body of evidence indicates a pivotal role of cognition and in particular executive function in gait control and fall prevention. In a recent gait study using electroencephalographic (EEG) imaging, we provided direct proof for cortical top-down inhibitory control in step adaptation. A crucial part of motor inhibition is recognizing stimuli that signal the need to inhibit or adjust motor actions such as steps during walking. One of the EEG signatures of performance monitoring in response to events signaling the need to adjust motor responses, are error-related potential (error-ERP) features. To examine whether error-ERP features may index executive control during gait adaptation, we analyzed high-density (108-channel) EEG data from an auditory gait pacing study. Participants (N = 18) walking on a steadily moving treadmill were asked to step in time to an auditory cue tone sequence, and then to quickly adapt their step length and rate, to regain step-cue synchrony following occasional unexpected shifts in the pacing cue train to a faster or slower cue tempo. Decomposition of the continuous EEG data by independent component analysis revealed a negative deflection in the source-resolved event-related potential (ERP) time locked to ‘late’ cue tones marking a shift to a slower cue tempo. This vertex-negative ERP feature, localized primarily to posterior medial frontal cortex (pMFC) and peaking 250 ms after the onset of the tempo-shift cue, we here refer to as the step-cue delay negativity (SDN). SDN source, timing, and polarity resemble other error-related ERP features, e. g. , the Error-Related Negativity (ERN) and Feedback-Related Negativity (FRN) in (seated) button press response tasks. In single trials, SDN amplitude varied with the magnitude of the cue latency deviation (the time interval between the expected and actual cue onsets). Regression analysis also identified linear coupling between SDN amplitude and the subsequent speed of gait tempo adaptation (as measured by the increase in length of the ensuing adaptation step). The SDN in this paradigm thus seems both to index the perceived need for and the subsequent magnitude of the immediate gait adjustment, consistent with performance-monitoring models. Future research might investigate relationships of these control processes to the impairment of gait adjustment in motor disorders and cognitive decline, for example to develop a biomarker for fall risk prediction in early-stage Parkinson's.

YNIMG Journal 2018 Journal Article

Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

• Fiorenzo Artoni
• Arnaud Delorme
• Scott Makeig

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered ‘dipolar’ ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.

YNIMG Journal 2018 Journal Article

Modeling brain dynamic state changes with adaptive mixture independent component analysis

• Sheng-Hsiou Hsu
• Luca Pion-Tonachini
• Jason Palmer
• Makoto Miyakoshi
• Scott Makeig
• Tzyy-Ping Jung

There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6–13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.

YNIMG Journal 2017 Journal Article

Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking

• Fiorenzo Artoni
• Chiara Fanciullacci
• Federica Bertolucci
• Alessandro Panarese
• Scott Makeig
• Silvestro Micera
• Carmelo Chisari

In lower mammals, locomotion seems to be mainly regulated by subcortical and spinal networks. On the contrary, recent evidence suggests that in humans the motor cortex is also significantly engaged during complex locomotion tasks. However, a detailed understanding of cortical contribution to locomotion is still lacking especially during stereotyped activities. Here, we show that cortical motor areas finely control leg muscle activation during treadmill stereotyped walking. Using a novel technique based on a combination of Reliable Independent Component Analysis, source localization and effective connectivity, and by combining electroencephalographic (EEG) and electromyographic (EMG) recordings in able-bodied adults we were able to examine for the first time cortical activation patterns and cortico-muscular connectivity including information flow direction. Results not only provided evidence of cortical activity associated with locomotion, but demonstrated significant causal unidirectional drive from contralateral motor cortex to muscles in the swing leg. These insights overturn the traditional view that human cortex has a limited role in the control of stereotyped locomotion, and suggest useful hypotheses concerning mechanisms underlying gait under other conditions. One sentence summary Motor cortex proactively drives contralateral swing leg muscles during treadmill walking, counter to the traditional view of stereotyped human locomotion.

YNIMG Journal 2016 Journal Article

ICA-derived cortical responses indexing rapid multi-feature auditory processing in six-month-old infants

• Caterina Piazza
• Chiara Cantiani
• Zeynep Akalin-Acar
• Makoto Miyakoshi
• April A. Benasich
• Gianluigi Reni
• Anna Maria Bianchi
• Scott Makeig

The abilities of infants to perceive basic acoustic differences, essential for language development, can be studied using auditory event-related potentials (ERPs). However, scalp-channel averaged ERPs sum volume-conducted contributions from many cortical areas, reducing the functional specificity and interpretability of channel-based ERP measures. This study represents the first attempt to investigate rapid auditory processing in infancy using independent component analysis (ICA), allowing exploration of source-resolved ERP dynamics and identification of ERP cortical generators. Here, we recorded 60-channel EEG data in 34 typically developing 6-month-old infants during a passive acoustic oddball paradigm presenting ‘standard’ tones interspersed with frequency- or duration-deviant tones. ICA decomposition was applied to single-subject EEG data. The best-fitting equivalent dipole or bilaterally symmetric dipole pair was then estimated for each resulting independent component (IC) process using a four-layer infant head model. Similar brain-source ICs were clustered across subjects. Results showed ERP contributions from auditory cortex and multiple extra-auditory cortical areas (often, bilaterally paired). Different cortical source combinations contributed to the frequency- and duration-deviant ERP peak sequences. For ICs in an ERP-dominant source cluster located in or near the mid-cingulate cortex, source-resolved frequency-deviant response N2 latency and P3 amplitude at 6 months-of-age predicted vocabulary size at 20 months-of-age. The same measures for scalp channel F6 (though not for other frontal channels) showed similar but weaker correlations. These results demonstrate the significant potential of ICA analyses to facilitate a deeper understanding of the neural substrates of infant sensory processing.

YNIMG Journal 2016 Journal Article

Simultaneous head tissue conductivity and EEG source location estimation

• Zeynep Akalin Acar
• Can E. Acar
• Scott Makeig

Accurate electroencephalographic (EEG) source localization requires an electrical head model incorporating accurate geometries and conductivity values for the major head tissues. While consistent conductivity values have been reported for scalp, brain, and cerebrospinal fluid, measured brain-to-skull conductivity ratio (BSCR) estimates have varied between 8 and 80, likely reflecting both inter-subject and measurement method differences. In simulations, mis-estimation of skull conductivity can produce source localization errors as large as 3cm. Here, we describe an iterative gradient-based approach to Simultaneous tissue Conductivity And source Location Estimation (SCALE). The scalp projection maps used by SCALE are obtained from near-dipolar effective EEG sources found by adequate independent component analysis (ICA) decomposition of sufficient high-density EEG data. We applied SCALE to simulated scalp projections of 15cm2-scale cortical patch sources in an MR image-based electrical head model with simulated BSCR of 30. Initialized either with a BSCR of 80 or 20, SCALE estimated BSCR as 32. 6. In Adaptive Mixture ICA (AMICA) decompositions of (45-min, 128-channel) EEG data from two young adults we identified sets of 13 independent components having near-dipolar scalp maps compatible with a single cortical source patch. Again initialized with either BSCR 80 or 25, SCALE gave BSCR estimates of 34 and 54 for the two subjects respectively. The ability to accurately estimate skull conductivity non-invasively from any well-recorded EEG data in combination with a stable and non-invasively acquired MR imaging-derived electrical head model could remove a critical barrier to using EEG as a sub-cm2-scale accurate 3-D functional cortical imaging modality.

YNIMG Journal 2015 Journal Article

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

• Yu Liao
• Zeynep Akalin Acar
• Scott Makeig
• Gedeon Deak

Contemporary active-EEG and EEG-imaging methods show particular promise for studying the development of action planning and social-action representation in infancy and early childhood. Action-related mu suppression was measured in eleven 3-year-old children and their mothers during a ‘live, ’ largely unscripted social interaction. High-density EEG was recorded from children and synchronized with motion-captured records of children's and mothers' hand actions, and with video recordings. Independent Component Analysis (ICA) was used to separate brain and non-brain source signals in toddlers' EEG records. EEG source dynamics were compared across three kinds of epochs: toddlers' own actions (execution), mothers' actions (observation), and between-turn intervals (no action). Mu (6–9Hz) power was suppressed in left and right somatomotor cortex during both action execution and observation, as reflected by independent components of individual children's EEG data. These mu rhythm components were accompanied by beta-harmonic (~16Hz) suppression, similar to findings from adults. The toddlers' power spectrum and scalp density projections provide converging evidence of adult-like mu-suppression features. Mu-suppression components' source locations were modeled using an age-specific 4-layer forward head model. Putative sources clustered around somatosensory cortex, near the hand/arm region. The results demonstrate that action-locked, event-related EEG dynamics can be measured, and source-resolved, from toddlers during social interactions with relatively unrestricted social behaviors.

TCS Journal 2015 Journal Article

Enumeration of BC-subtrees of trees

• Yu Yang
• Hongbo Liu
• Hua Wang
• Scott Makeig

A BC-tree (block-cutpoint-tree) is a tree (with at least two vertices) where the distance between any two leaves is even. Motivated from the study of the “core” of a graph, BC-trees form an interesting class of trees. We provide a comprehensive study of questions related to BC-trees. As the analogue of the study of extremal questions on subtrees of trees, we first characterize the general extremal trees that maximize or minimize the number of BC-subtrees or leaf-containing BC-subtrees. We further discuss the “middle part” of a tree with respect to the number of BC-subtrees, namely the BC-subtree-core that behaves in a rather different way than all previously known “middle parts” of a tree. Last but not least, fast algorithms are proposed (following similar ideas as those of the enumeration of subtrees) for enumerating various classes of BC-subtrees of a tree.

YNICL Journal 2014 Journal Article

Cortical substrates and functional correlates of auditory deviance processing deficits in schizophrenia

• Anthony J. Rissling
• Makoto Miyakoshi
• Catherine A. Sugar
• David L. Braff
• Scott Makeig
• Gregory A. Light

Although sensory processing abnormalities contribute to widespread cognitive and psychosocial impairments in schizophrenia (SZ) patients, scalp-channel measures of averaged event-related potentials (ERPs) mix contributions from distinct cortical source-area generators, diluting the functional relevance of channel-based ERP measures. SZ patients (n = 42) and non-psychiatric comparison subjects (n = 47) participated in a passive auditory duration oddball paradigm, eliciting a triphasic (Deviant-Standard) tone ERP difference complex, here termed the auditory deviance response (ADR), comprised of a mid-frontal mismatch negativity (MMN), P3a positivity, and re-orienting negativity (RON) peak sequence. To identify its cortical sources and to assess possible relationships between their response contributions and clinical SZ measures, we applied independent component analysis to the continuous 68-channel EEG data and clustered the resulting independent components (ICs) across subjects on spectral, ERP, and topographic similarities. Six IC clusters centered in right superior temporal, right inferior frontal, ventral mid-cingulate, anterior cingulate, medial orbitofrontal, and dorsal mid-cingulate cortex each made triphasic response contributions. Although correlations between measures of SZ clinical, cognitive, and psychosocial functioning and standard (Fz) scalp-channel ADR peak measures were weak or absent, for at least four IC clusters one or more significant correlations emerged. In particular, differences in MMN peak amplitude in the right superior temporal IC cluster accounted for 48% of the variance in SZ-subject performance on tasks necessary for real-world functioning and medial orbitofrontal cluster P3a amplitude accounted for 40%/54% of SZ-subject variance in positive/negative symptoms. Thus, source-resolved auditory deviance response measures including MMN may be highly sensitive to SZ clinical, cognitive, and functional characteristics.

YNIMG Journal 2014 Journal Article

Cortical surface alignment in multi-subject spatiotemporal independent EEG source imaging

• Arthur C. Tsai
• Tzyy-Ping Jung
• Vincent S.C. Chien
• Alexander N. Savostyanov
• Scott Makeig

Brain responses to stimulus presentations may vary widely across subjects in both time course and spatial origins. Multi-subject EEG source imaging studies that apply Independent Component Analysis (ICA) to data concatenated across subjects have overlooked the fact that projections to the scalp sensors from functionally equivalent cortical sources vary from subject to subject. This study demonstrates an approach to spatiotemporal independent component decomposition and alignment that spatially co-registers the MR-derived cortical topographies of individual subjects to a well-defined, shared spherical topology (Fischl et al. , 1999). Its efficacy for identifying functionally equivalent EEG sources in multi-subject analysis is demonstrated by analyzing EEG and behavioral data from a stop-signal paradigm using two source-imaging approaches, both based on individual subject independent source decompositions. The first, two-stage approach uses temporal infomax ICA to separate each subject's data into temporally independent components (ICs), then estimates the source density distribution of each IC process from its scalp map and clusters similar sources across subjects (Makeig et al. , 2002). The second approach, Electromagnetic Spatiotemporal Independent Component Analysis (EMSICA), combines ICA decomposition and source current density estimation of the artifact-rejected data into a single spatiotemporal ICA decomposition for each subject (Tsai et al. , 2006), concurrently identifying both the spatial source distribution of each cortical source and its event-related dynamics. Applied to the stop-signal task data, both approaches gave IC clusters that separately accounted for EEG processes expected in stop-signal tasks, including pre/postcentral mu rhythms, anterior-cingulate theta rhythm, and right-inferior frontal responses, the EMSICA clusters exhibiting more tightly correlated source areas and time-frequency features.

YNIMG Journal 2014 Journal Article

RELICA: A method for estimating the reliability of independent components

• Fiorenzo Artoni
• Danilo Menicucci
• Arnaud Delorme
• Scott Makeig
• Silvestro Micera

Independent Component Analysis (ICA) is a widely applied data-driven method for parsing brain and non-brain EEG source signals, mixed by volume conduction to the scalp electrodes, into a set of maximally temporally and often functionally independent components (ICs). Many ICs may be identified with a precise physiological or non-physiological origin. However, this process is hindered by partial instability in ICA results that can arise from noise in the data. Here we propose RELICA (RELiable ICA), a novel method to characterize IC reliability within subjects. RELICA first computes IC “dipolarity” a measure of physiological plausibility, plus a measure of IC consistency across multiple decompositions of bootstrap versions of the input data. RELICA then uses these two measures to visualize and cluster the separated ICs, providing a within-subject measure of IC reliability that does not involve checking for its occurrence across subjects. We demonstrate the use of RELICA on EEG data recorded from 14 subjects performing a working memory experiment and show that many brain and ocular artifact ICs are correctly classified as “stable” (highly repeatable across decompositions of bootstrapped versions of the input data). Many stable ICs appear to originate in the brain, while other stable ICs account for identifiable non-brain processes such as line noise. RELICA might be used with any linear blind source separation algorithm to reduce the risk of basing conclusions on unstable or physiologically un-interpretable component processes.

YNIMG Journal 2013 Journal Article

Measure projection analysis: A probabilistic approach to EEG source comparison and multi-subject inference

• Nima Bigdely-Shamlo
• Tim Mullen
• Kenneth Kreutz-Delgado
• Scott Makeig

A crucial question for the analysis of multi-subject and/or multi-session electroencephalographic (EEG) data is how to combine information across multiple recordings from different subjects and/or sessions, each associated with its own set of source processes and scalp projections. Here we introduce a novel statistical method for characterizing the spatial consistency of EEG dynamics across a set of data records. Measure Projection Analysis (MPA) first finds voxels in a common template brain space at which a given dynamic measure is consistent across nearby source locations, then computes local-mean EEG measure values for this voxel subspace using a statistical model of source localization error and between-subject anatomical variation. Finally, clustering the mean measure voxel values in this locally consistent brain subspace finds brain spatial domains exhibiting distinguishable measure features and provides 3-D maps plus statistical significance estimates for each EEG measure of interest. Applied to sufficient high-quality data, the scalp projections of many maximally independent component (IC) processes contributing to recorded high-density EEG data closely match the projection of a single equivalent dipole located in or near brain cortex. We demonstrate the application of MPA to a multi-subject EEG study decomposed using independent component analysis (ICA), compare the results to k-means IC clustering in EEGLAB (sccn. ucsd. edu/eeglab), and use surrogate data to test MPA robustness. A Measure Projection Toolbox (MPT) plug-in for EEGLAB is available for download (sccn. ucsd. edu/wiki/MPT). Together, MPA and ICA allow use of EEG as a 3-D cortical imaging modality with near-cm scale spatial resolution.

YNIMG Journal 2011 Journal Article

Electrocortical activity is coupled to gait cycle phase during treadmill walking

• Joseph T. Gwin
• Klaus Gramann
• Scott Makeig
• Daniel P. Ferris

Recent findings suggest that human cortex is more active during steady-speed unperturbed locomotion than previously thought. However, techniques that have been used to image the brain during locomotion lack the temporal resolution necessary to assess intra-stride cortical dynamics. Our aim was to determine if electrocortical activity is coupled to gait cycle phase during steady-speed human walking. We used electroencephalography (EEG), motion capture, and a force-measuring treadmill to record brain and body dynamics while eight healthy young adult subjects walked on a treadmill. Infomax independent component analysis (ICA) parsed EEG signals into maximally independent component (IC) processes representing electrocortical sources, muscle sources, and artifacts. We calculated a spatially fixed equivalent current dipole for each IC using an inverse modeling approach, and clustered electrocortical sources across subjects by similarities in dipole locations and power spectra. We then computed spectrograms for each electrocortical source that were time-locked to the gait cycle. Electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex exhibited significant (p <0. 05) intra-stride changes in spectral power. During the end of stance, as the leading foot was contacting the ground and the trailing foot was pushing off, alpha- and beta-band spectral power increased in or near the left/right sensorimotor and dorsal anterior cingulate cortex. Power increases in the left/right sensorimotor cortex were more pronounced for contralateral limb push-off (ipsilateral heel-strike) than for ipsilateral limb push-off (contralateral heel-strike). Intra-stride high-gamma spectral power changes were evident in anterior cingulate, posterior parietal, and sensorimotor cortex. These data confirm cortical involvement in steady-speed human locomotion. Future applications of these techniques could provide critical insight into the neural mechanisms of movement disorders and gait rehabilitation.

YNIMG Journal 2009 Journal Article

Identifying reliable independent components via split-half comparisons

• David M. Groppe
• Scott Makeig
• Marta Kutas

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the method's validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline.

YNIMG Journal 2008 Journal Article

Tonic and phasic electroencephalographic dynamics during continuous compensatory tracking

• Ruey-Song Huang
• Tzyy-Ping Jung
• Arnaud Delorme
• Scott Makeig

Tonic and phasic dynamics of electroencephalographic (EEG) activities during a continuous compensatory tracking task (CTT) were analyzed using time–frequency analysis of EEG sources identified by independent component analysis (ICA). In 1-hour sessions, 70-channel EEG data were recorded while participants attempted to use frequent compensatory trackball movements to maintain a drifting disc close to a bulls-eye at screen center. Disc trajectories were converted into two moving-average performance measures, root mean square distance of the disc from screen center in 4-s (‘local’) and in 20-s (‘global’) moving time windows. Maximally independent EEG processes and their equivalent dipole source locations were obtained using the EEGLAB toolbox (http: //sccn. ucsd. edu/eeglab). Across subjects and sessions, independent EEG processes in occipital, somatomotor, and supplementary motor cortices exhibited tonic power increases during periods of high tracking error, plus additional phasic power increases in several frequency bands before and after trackball movements following disc ‘perigees’ (moments at which the disc began to drift away from the bulls-eye). These phasic activity increases, which were larger during high-error periods, reveal an intimate relation between EEG dynamics and top–down recognition of responding to threatening events. Thus during a continuous tracking task without impulsive stimulus onsets, sub-second scale EEG dynamics related to visuomotor task could be dissociated from slower spectral modulations linked to changes in performance and arousal. We tentatively interpret the observed EEG signal increases as indexing tonic and phasic modulations of the levels of task attention and engagement required to maintain visuomotor performance during sustained performance.

YNIMG Journal 2007 Journal Article

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

• Arnaud Delorme
• Terrence Sejnowski
• Scott Makeig

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (−50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.

NeurIPS Conference 2006 Conference Paper

Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization

• Rey Ramírez
• Jason Palmer
• Scott Makeig
• Bhaskar Rao
• David Wipf

The ill-posed nature of the MEG/EEG source localization problem requires the incorporation of prior assumptions when choosing an appropriate solution out of an infinite set of candidates. Bayesian methods are useful in this capacity because they allow these assumptions to be explicitly quantified. Recently, a number of empirical Bayesian approaches have been proposed that attempt a form of model selection by using the data to guide the search for an appropriate prior. While seemingly quite different in many respects, we apply a unifying framework based on automatic relevance determination (ARD) that elucidates various attributes of these methods and suggests directions for improvement. We also derive theoretical properties of this methodology related to convergence, local minima, and localization bias and explore connections with established algorithms.

YNIMG Journal 2006 Journal Article

Mapping single-trial EEG records on the cortical surface through a spatiotemporal modality

• Arthur C. Tsai
• Michelle Liou
• Tzyy-Ping Jung
• Julie A. Onton
• Philip E. Cheng
• Chien-Chih Huang
• Jeng-Ren Duann
• Scott Makeig

Event-related potentials (ERPs) induced by visual perception and cognitive tasks have been extensively studied in neuropsychological experiments. ERP activities time-locked to stimulus presentation and task performance are often observed separately at individual scalp channels based on averaged time series across epochs and experimental subjects. An analysis using averaged EEG dynamics could discount information regarding interdependency between ongoing EEG and salient ERP features. Advanced tools such as independent component analysis (ICA) have been developed for decomposing collections of single-trial EEG records into separate features. Those features (or independent components) can then be mapped onto the cortical surface using source localization algorithms to visualize brain activation maps and to study between-subject consistency. In this study, we propose a statistical framework for estimating the time course of spatiotemporally independent EEG components simultaneously with their cortical distributions. Within this framework, we implemented Bayesian spatiotemporal analysis for imaging the sources of EEG features on the cortical surface. The framework allows researchers to include prior knowledge regarding spatial locations as well as spatiotemporal independence of different EEG sources. The use of the Electromagnetic Spatiotemporal ICA (EMSICA) method is illustrated by mapping event-related EEG dynamics induced by events in a visual two-back continuous performance task. The proposed method successfully identified several interesting components with plausible corresponding cortical activation topographies, including processes contributing to the late positive complex (LPC) located in central parietal, frontal midline, and anterior cingulate cortex, to atypical mu rhythms associated with the precentral gyrus, and to the central posterior alpha activity in the precuneus.

YNIMG Journal 2005 Journal Article

Frontal midline EEG dynamics during working memory

• Julie Onton
• Arnaud Delorme
• Scott Makeig

We show that during visual working memory, the electroencephalographic (EEG) process producing 5–7 Hz frontal midline theta (fmθ) activity exhibits multiple spectral modes involving at least three frequency bands and a wide range of amplitudes. The process accounting for the fmθ increase during working memory was separated from 71-channel data by clustering on time/frequency transforms of components returned by independent component analysis (ICA). Dipole models of fmθ component scalp maps were consistent with their generation in or near dorsal anterior cingulate cortex. From trial to trial, theta power of fmθ components varied widely but correlated moderately with theta power in other frontal and left temporal processes. The weak mean increase in frontal midline theta power with increasing memory load, produced entirely by the fmθ components, largely reflected progressively stronger theta activity in a relatively small proportion of trials. During presentations of letter series to be memorized or ignored, fmθ components also exhibited 12–15 Hz low-beta activity that was stronger during memorized than during ignored letter trials, independent of letter duration. The same components produced a brief 3-Hz burst 500 ms after onset of the Probe letter following each letter sequence. A new decomposition method, log spectral ICA, applied to normalized log time/frequency transforms of fmθ component Memorize-letter trials, showed that their low-beta activity reflected harmonic energy in continuous, sharp-peaked theta wave trains as well as independent low-beta bursts. Possibly, the observed fmθ process variability may index dynamic adjustments in medial frontal cortex to trial-specific behavioral context and task demands.

YNIMG Journal 2002 Journal Article

Single-Trial Variability in Event-Related BOLD Signals

• Jeng-Ren Duann
• Tzyy-Ping Jung
• Wen-Jui Kuo
• Tzu-Chen Yeh
• Scott Makeig
• Jen-Chuen Hsieh
• Terrence J. Sejnowski

Most current analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) to experimental stimuli or events in brain areas of interest. In addition, they typically assume homogeneity of both the HR and the non-HR “noise” signals, both across brain regions and across similar experimental events. When HRs vary unpredictably, from area to area or from trial to trial, an alternative approach is needed. Here, we use Infomax independent component analysis (ICA) to detect and visualize variations in single-trial HRs in event-related fMRI data. Six subjects participated in four fMRI sessions each in which ten bursts of 8-Hz flickering-checkerboard stimulation were presented for 0. 5-s (short) or 3-s (long) durations at 30-s intervals. Five axial slices were acquired by a Bruker 3-T magnetic resonance imager at interscan intervals of 500 ms (TR). ICA decomposition of the resulting blood oxygenation level-dependent (BOLD) data from each session produced an independent component active in primary visual cortex (V1) and, in several sessions, another active in medial temporal cortex (MT/V5). Visualizing sets of BOLD response epochs with novel BOLD-image plots demonstrated that component HRs varied substantially and often systematically across trials as well as across sessions, subjects, and brain areas. Contrary to expectation, in four of the six subjects the V1 component HR contained two positive peaks in response to short-stimulus bursts, while components with nearly identical regions of activity in long-stimulus sessions from the same subjects were associated with single-peaked HRs. Thus, ICA combined with BOLD-image visualization can reveal dramatic and unforeseen HR variations not apparent to researchers analyzing their data with event-related response averaging and fixed HR templates.

YNIMG Journal 2001 Journal Article

Blind decomposition reveals novel hemodynamics response features

• Jeng-Ren Duann
• Tzyy-Ping Jung
• Wen-Jui Kuo
• Tzu-Chen Yeh
• Scott Makeig
• Jen-Chuen Hsieh
• Terrence Sejnowski

YNIMG Journal 2001 Journal Article

Imaging event-related brain dynamics

• Scott Makeig
• Tzyy-Ping Jung
• Marissa Westerfield
• Terrence J. Sejnowski

NeurIPS Conference 1998 Conference Paper

Analyzing and Visualizing Single-Trial Event-Related Potentials

• Tzyy-Ping Jung
• Scott Makeig
• Marissa Westerfield
• Jeanne Townsend
• Eric Courchesne
• Terrence Sejnowski

Event-related potentials (ERPs), are portions of electroencephalo(cid: 173) graphic (EEG) recordings that are both time- and phase-locked to experimental events. ERPs are usually averaged to increase their signal/noise ratio relative to non-phase locked EEG activ(cid: 173) ity, regardless of the fact that response activity in single epochs may vary widely in time course and scalp distribution. This study applies a linear decomposition tool, Independent Component Anal(cid: 173) ysis (ICA) [1], to multichannel single-trial EEG records to derive spatial filters that decompose single-trial EEG epochs into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain networks. Our results on normal and autistic subjects show that ICA can sep(cid: 173) arate artifactual, stimulus-locked, response-locked, and. non-event related background EEG activities into separate components, al(cid: 173) lowing ( 1) removal of pervasive artifacts of all types from single-trial EEG records, and (2) identification of both stimulus- and response(cid: 173) locked EEG components. Second, this study proposes a new visual(cid: 173) ization tool, the 'ERP image', for investigating variability in laten(cid: 173) cies and amplitudes of event-evoked responses in spontaneous EEG or MEG records. We show that sorting single-trial ERP epochs in order of reaction time and plotting the potentials in 2-D clearly reveals underlying patterns of response variability linked to per(cid: 173) formance. These analysis and visualization tools appear broadly applicable to electrophyiological research on both normal and clin(cid: 173) ical populations. Analyzing and Visualizing Single-Trial Event-Related Potentials

NeurIPS Conference 1997 Conference Paper

Extended ICA Removes Artifacts from Electroencephalographic Recordings

• Tzyy-Ping Jung
• Colin Humphries
• Te-Won Lee
• Scott Makeig
• Martin McKeown
• Vicente Iragui
• Terrence Sejnowski

Severe contamination of electroencephalographic (EEG) activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. Rejecting contami(cid: 173) nated EEG segments results in a considerable loss of information and may be impractical for clinical data. Many methods have been proposed to remove eye movement and blink artifacts from EEG recordings. Often regression in the time or frequency domain is performed on simultaneous EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. However, EOG records also contain brain signals [1, 2], so regressing out EOG ac(cid: 173) tivity inevitably involves subtracting a portion of the relevant EEG signal from each recording as well. Regression cannot be used to remove muscle noise or line noise, since these have no reference channels. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records. The method is based on an extended version of a previous Indepen(cid: 173) dent Component Analysis (lCA) algorithm [3, 4] for performing blind source separation on linear mixtures of independent source signals with either sub-Gaussian or super-Gaussian distributions. Our results show that ICA can effectively detect, separate and re(cid: 173) move activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression-based methods. Extended leA Removes Artifacts from EEG Recordings

NeurIPS Conference 1995 Conference Paper

Independent Component Analysis of Electroencephalographic Data

• Scott Makeig
• Anthony Bell
• Tzyy-Ping Jung
• Terrence Sejnowski

Because of the distance between the skull and brain and their differ(cid: 173) ent resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smearing of EEG data by volume conduction does not involve significant time delays, however, sug(cid: 173) gesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski [1] is suitable for performing blind source sep(cid: 173) aration on EEG data. The ICA algorithm separates the problem of source identification from that of source localization. First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show: (1) ICA training is insensitive to different random seeds. (2) ICA may be used to segregate obvious artifactual EEG components (line and muscle noise, eye movements) from other sources. (3) ICA is capable of isolating overlapping EEG phenomena, including al(cid: 173) pha and theta bursts and spatially-separable ERP components, to separate ICA channels. (4) N onstationarities in EEG and behav(cid: 173) ioral state can be tracked using ICA via changes in the amount of residual correlation between ICA-filtered output channels. 146 S. MAKEIG, A. l. BELL, T. -P. lUNG, T. l. SEJNOWSKI

NeurIPS Conference 1995 Conference Paper

Using Feedforward Neural Networks to Monitor Alertness from Changes in EEG Correlation and Coherence

• Scott Makeig
• Tzyy-Ping Jung
• Terrence Sejnowski

We report here that changes in the normalized electroencephalo(cid: 173) graphic (EEG) cross-spectrum can be used in conjunction with feedforward neural networks to monitor changes in alertness of op(cid: 173) erators continuously and in near-real time. Previously, we have shown that EEG spectral amplitudes covary with changes in alert(cid: 173) ness as indexed by changes in behavioral error rate on an auditory detection task [6, 4]. Here, we report for the first time that increases in the frequency of detection errors in this task are also accompa(cid: 173) nied by patterns of increased and decreased spectral coherence in several frequency bands and EEG channel pairs. Relationships between EEG coherence and performance vary between subjects, but within subjects, their topographic and spectral profiles appear stable from session to session. Changes in alertness also covary with changes in correlations among EEG waveforms recorded at different scalp sites, and neural networks can also estimate alert(cid: 173) ness from correlation changes in spontaneous and unobtrusively(cid: 173) recorded EEG signals.