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Open Access Publications from the University of California

Scholarly Works (47 results)

  • Article
  • Peer Reviewed
UC San Diego Previously Published Works (2023)
Automated preprocessing methods are critically needed to process the large publicly-available EEG databases, but the optimal approach remains unknown because we lack data quality metrics to compare them. Here, we designed a simple yet robust EEG data quality metric assessing the percentage of significant channels between two experimental conditions within a 100 ms post-stimulus time range. Because of volume conduction in EEG, given no noise, most brain-evoked related potentials (ERP) should be visible on every single channel. Using three publicly available collections of EEG data, we showed that, with the exceptions of high-pass filtering and bad channel interpolation, automated data corrections had no effect on or significantly decreased the percentage of significant channels. Referencing and advanced baseline removal methods were significantly detrimental to performance. Rejecting bad data segments or trials could not compensate for the loss in statistical power. Automated Independent Component Analysis rejection of eyes and muscles failed to increase performance reliably. We compared optimized pipelines for preprocessing EEG data maximizing ERP significance using the leading open-source EEG software: EEGLAB, FieldTrip, MNE, and Brainstorm. Only one pipeline performed significantly better than high-pass filtering the data.
    Cover page: EEG is better left alone
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2004)

    We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment(The Math-works, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation. (C) 2003 Elsevier B.V. All rights reserved.

      Cover page: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis
      • Article
      Research Reports (2001)

      PATH AVCSS researches have been traditionally oriented toward automatic vehicle design. Recently, the field of investigation has been extended from Automated Highway System (AHS) to assistance driving systems. One of the tools built at PATH for automatic system design and assessment is SmartAHS. SmartAHS is a micro-simulation tool dedicated to the simulation of automatic vehicles and has shown to be very useful for fully automatic system simulation. These simulations permit researchers to evaluate the impact of such system on throughput improvement. In order to apply the same method to the design of partially automated systems, it is necessary to develop a human driver component for this simulation tool. This component needs to allow the comparison of human driving characteristics versus automated vehicles in the long term, but first, it has to permit the production, for simulation purposes, of a realistic human driving behavior.The goal of the modeling effort presented here is twofold. On one hand, there is an objective to design and evaluate AVCSS at a driver level (respective of human processing constraints), which imply a consideration of the cognitive processes involved while driving. On the other hand, there is also a goal to integrate of the model to a micro-simulation tool, for evaluation of AVCSS at traffic level, and more specifically in terms of throughput evolution. This second goal implies the consideration of vehicle modelsand control of the vehicle. SmartAHS mainly consists of automatically or semi-automatically controlled vehicles. This is why this component is called human driver model (as opposed to automated or semi-automated vehicles). The method preferred for the realization of this model is a capitalization of these various approaches by the application of a driver cognitive model, COSMODRIVE (COgnitive Simulation MOdel of the DRIVEr) (Bellet, 1998). This model conceptual framework is a skeleton around which can be organized the relevant aspects of the different approaches for the purpose of driver modeling. The general architecture of this model will be presented first, with a detailed description of the modules content and exchanges. In Section 2, the implemented modules and procedure of implementation will be described. The third section will be the description of the simulation realized with the model, for both normal driving and emergency case. Finally, this report will conclude with the description of a calibration procedure for part of the model.

        Cover page: Human Driver Model for SmartAHS
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2010)
        Long-term Vipassana meditators sat in meditation vs. a control rest (mind-wandering) state for 21 min in a counterbalanced design with spontaneous EEG recorded. Meditation state dynamics were measured with spectral decomposition of the last 6 min of the eyes-closed silent meditation compared to control state. Meditation was associated with a decrease in frontal delta (1–4 Hz) power, especially pronounced in those participants not reporting drowsiness during meditation. Relative increase in frontal theta (4–8 Hz) power was observed during meditation, as well as significantly increased parieto-occipital gamma (35–45 Hz) power, but no other state effects were found for the theta (4–8 Hz), alpha (8–12 Hz), or beta (12–25 Hz) bands. Alpha power was sensitive to condition order, and more experienced meditators exhibited no tendency toward enhanced alpha during meditation relative to the control task. All participants tended to exhibit decreased alpha in association with reported drowsiness. Cross-experimental session occipital gamma power was the greatest in meditators with a daily practice of 10+ years, and the meditation-related gamma power increase was similarly the strongest in such advanced practitioners. The findings suggest that long-term Vipassana meditation contributes to increased occipital gamma power related to long-term meditational expertise and enhanced sensory awareness.
          Cover page: Occipital gamma activation during Vipassana meditation
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2019)
          Here we present an electroencephalographic (EEG) collection of 71-channel datasets recorded from 14 subjects (7 males, 7 females, aged 20-40 years) while performing a visual working memory task with a T set of 150 Independent Component Analysis (ICA) decompositions by Extended Infomax using RELICA, each on a bootstrap resampling of the data. These data are linked to the paper "Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition" [1]. Independent components (ICs) are clustered within subject and thereby associated with a quality index (QIc) measure of their stability to data resampling. Sets of single ICA decompositions obtained after applying Principal Component Analysis (PCA) to the data to perform dimension reduction retaining (85%, 95%, 99%) of data variance are also included, as are the positions of the best fitting equivalent dipoles for ICs whose scalp projections are compatible with a compact brain source. These bootstrap ICs may be used as benchmarks for different data preprocessing pipelines and/or ICA algorithms, allowing investigation of the effects that noise or insufficient data have on the quality of ICA decompositions.
            Cover page: A visual working memory dataset collection with bootstrap Independent Component Analysis for comparison of electroencephalographic preprocessing pipelines
            • Article
            • Peer Reviewed
            UC Davis Previously Published Works (2023)
            Circadian (24-h) rhythms in the suprachiasmatic nucleus (SCN) are established in utero in rodents, but rhythmicity of peripheral circadian clocks appears later in postnatal development. Since peripheral oscillators can be influenced by maternal feeding and behavior, we investigated whether exposure to the adverse environmental conditions of limited bedding (LB) during postnatal life would alter rhythmicity in the SCN, adrenal gland and liver in neonatal (postnatal day PND10), juvenile (PND28) and adult rats. We also examined locomotor activity in adults. Limited bedding increased nursing time and slightly increased fragmentation of maternal behavior. Exposure to LB reduced the amplitude of Per2 in the SCN on PND10. Adrenal clock gene expression (Bmal1, Per2, Cry1, Rev-erbα, Dbp) and corticosterone secretion were rhythmic at all ages in NB offspring, whereas rhythmicity of Bmal1, Cry1 and corticosterone was abolished in neonatal LB pups. Circadian gene expression in the adrenal and liver was well established by PND28. In adults, liver expression of several circadian genes was increased at specific daytimes by LB and the microstructure of locomotor behavior was altered. Thus, changes in maternal care and behavior might provide important signals to the maturing peripheral oscillators and modify, in particular their output functions in the long-term.
              Cover page: Peripheral clock gene oscillations are perturbed in neonatal and adult rat offspring raised under adverse limited bedding conditions.
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2021)
              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).
                Cover page: Capturing the nature of events and event context using hierarchical event descriptors (HED)
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2012)
                Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).
                  Cover page: Independent EEG Sources Are Dipolar
                  • Article
                  • Peer Reviewed
                  UC San Diego Previously Published Works (2021)
                  Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other's work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows). Here we show that this can easily be done using EEGLAB and tools built around it. BIDS tools allow importing all the necessary information and create a study from electroencephalography (EEG)-Brain Imaging Data Structure compliant data. From there preprocessing can be carried out in only a few steps using EEGLAB and statistical analyses performed using the LIMO EEG plug-in. Using Wakeman and Henson (2015) face dataset, we illustrate how to prepare data and build different statistical models, a standard factorial design (faces ∗ repetition), and a more modern trial-based regression approach for the stimulus repetition effect, all in a few reproducible command lines.
                    Cover page: From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG
                    • Article
                    • Peer Reviewed
                    UC Irvine Previously Published Works (2013)
                      Cover page: Plasticity of visual attention in Isha yoga meditation practitioners before and after a 3-month retreatCreative Commons 'BY' version 4.0 license
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