Multiresolution Analysis of Event-Related Potentials by Wavelet Decomposition

ObjectiveCluster analysis of spatio-temporal event-related potential (ERP) data is a promising tool for exploring the measurement time window of ERPs. However, even after preprocessing, the remaining noise can result in uncertain cluster maps followed by unreliable time windows while clustering via conventional clustering methods. MethodsWe designed an ensemble deep clustering pipeline to determine a reliable time window for the ERP of interest from temporal concatenated grand average ERP data. The proposed pipeline includes semi-supervised deep clustering methods initialized by consensus clustering and unsupervised deep clustering methods with end-to-end architectures. Ensemble clustering from those deep clusterings was used by the designed adaptive time window determination to estimate the time window. ResultsAfter applying simulated and real ERP data, our method successfully obtained the time window for identifying the P3 components (as the interest of both ERP studies) while additional noise (e.g., adding 20 dB to −5 dB white Gaussian noise) was added to the prepared data. ConclusionCompared to the state-of-the-art clustering methods, a superior clustering performance was yielded from both ERP data. Furthermore, more stable and precise time windows were elicited as the noise increased. SignificanceOur study provides a complementary understanding of identifying the cognitive process using deep clustering analysis to the existing studies. Our finding suggests that deep clustering can be used to identify the ERP of interest when the data is imperfect after preprocessing.

Event Abstract Back to Event Brain pathways underlying Response Inhibition and Response Caution Renate Thienel1*, Elise Mansfield1, Patrick S. Cooper1, Andrew Heathcote1, Birte Forstmann2, Pat Michie1, Gavin Cooper1 and Frini Karayanidis1 1 School of Psychology, University of Newcastle, Australia 2 University of Amsterdam, Netherlands Background: Our previous findings suggest that the striatum is instrumental in mediating response caution in a task switching paradigm (Mansfield et al., J. Neurosci 2011, 31:14688-92), and that structural integrity in two separate fronto-striatal networks are associated with adopting an overall more risky or more cautious response threshold. In the present study, we examine whether a similar network underlies response inhibition, as operationalized by a stop signal task. Response inhibition has been shown to be mediated by cortical and subcortical structures within the inferior frontal lobe and the subthalamic nucleus, respectively. We investigate whether structural integrity measures in these regions can explain the relationship between stop signal related event related potential (ERP) components and estimates of stop signal reaction time (SSRT). Finally the relationship between, and the differential structural pathways contributing to, response threshold adjustment (task-switching paradigm) and stop signal response time will be examined. Methods: Diffusion weighted Imaging (DWI), ERP and behavioural data has been collected in 16 healthy participants (24.5 years, 5 male). A correlational analysis was performed for the ERPs and the behavioural measures of both tasks (task switching and stop signal task). Tract based spatial statistics (TBSS) of the DWI data was performed to compare the white matter pathways associated with response thresholds adjustment (task-switching task), and response inhibition (stop-signal task) respectively. These regions were then used to explain variation in ERP data. Results/Discussion: The integrity within distinct structural pathways (DWI data) explained the relationship between (1) ERP measures and response threshold adjustment and (2) ERP measures and SSRTs. Distinct fronto-basal ganglia pathways mediated threshold setting and response suppression. Keywords: task-switching, stop signal task, DWI, fronto-striatal circuits, response inhibition Conference: ACNS-2013 Australasian Cognitive Neuroscience Society Conference, Clayton, Melbourne, Australia, 28 Nov - 1 Dec, 2013. Presentation Type: Poster Topic: Executive Processes Citation: Thienel R, Mansfield E, Cooper PS, Heathcote A, Forstmann B, Michie P, Cooper G and Karayanidis F (2013). Brain pathways underlying Response Inhibition and Response Caution. Conference Abstract: ACNS-2013 Australasian Cognitive Neuroscience Society Conference. doi: 10.3389/conf.fnhum.2013.212.00145 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 Sep 2013; Published Online: 25 Nov 2013. * Correspondence: Dr. Renate Thienel, School of Psychology, University of Newcastle, Newcastle, Australia, renate.thienel@newcastle.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Renate Thienel Elise Mansfield Patrick S Cooper Andrew Heathcote Birte Forstmann Pat Michie Gavin Cooper Frini Karayanidis Google Renate Thienel Elise Mansfield Patrick S Cooper Andrew Heathcote Birte Forstmann Pat Michie Gavin Cooper Frini Karayanidis Google Scholar Renate Thienel Elise Mansfield Patrick S Cooper Andrew Heathcote Birte Forstmann Pat Michie Gavin Cooper Frini Karayanidis PubMed Renate Thienel Elise Mansfield Patrick S Cooper Andrew Heathcote Birte Forstmann Pat Michie Gavin Cooper Frini Karayanidis Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

How to validate similarity in linear transform models of event-related potentials between experimental conditions?

We present MINEMO (Minimal Information for Neural ElectroMagnetic Ontologies), a checklist for the description of event-related potentials (ERP) studies. MINEMO extends MINI (Minimal Information for Neuroscience Investigations)to the ERP domain. Checklist terms are explicated in NEMO, a formal ontology that is designed to support ERP data sharing and integration. MINEMO is also linked to an ERP database and web application (the NEMO portal). Users upload their data and enter MINEMO information through the portal. The database then stores these entries in RDF (Resource Description Framework), along with summary metrics, i.e., spatial and temporal metadata. Together these spatial, temporal, and functional metadata provide a complete description of ERP data and the context in which these data were acquired. The RDF files then serve as inputs to ontology-based labeling and meta-analysis. Our ultimate goal is to represent ERPs using a rich semantic structure, so results can be queried at multiple levels, to stimulate novel hypotheses and to promote a high-level, integrative account of ERP results across diverse study methods and paradigms.

Earlier research at the Navy Personnel Research and Development Center revealed that measures of the brain response to sensory stimuli, known as Event Related Potentials (ERP) may be used to assess unique process-related variance that is dependent upon human performance. For example, it was found that the sensitivity of individual subjects to dynamic color contrast in computer displays can be assessed by visual ERP's. It has also been observed that RMS measures of the P1-N1-P2 complex and the P300 component of the ERP are related to signal detection and classification measures of performance. The present effort is to classify the ERP response to stimuli followed by a motor action of the subject, using neural networks. The stimuli consist of flashing a light which could be either one of two distinct colors. The following motor-action corresponds to pushing one of the two available buttons representing the two colors. The features will be selected from the Fast Walsh Transformation (FWT) of ERP observations and will be applied to appropriate Neural Networks to obtain a prediction of a possible response. Since a small number of features is derived from a large section of available processed data and since the ERP data is diluted with noise and artifacts, there is a need to evaluate the effectiveness of this data compression towards efficient prediction of the future action of the subject. The present paper deals with this study.

Electroencephalography (EEG) is a non-invasive monitoring method that tracks and records the neural activities of the brain. The time-locked capture of the EEG to the external stimuli is known as Event-Related Potential (ERP) and it can help elucidate how the brain responds to the stimuli. In general, EEG is an uneven mixture of neural and non-neural sources of activities and these non-neural (non-EEG) signals produce artifacts in the EEG that can decrease the SNR in experiments and may lead to erroneous conclusions about the effects of experimental manipulation. Thus, it is very important to remove artifacts from the recorded EEG prior to analysis. The most common artifacts impacting ERPs are eye-blink, eye-movement, and body-movement. These artifacts-corrupted data can be removed by visual inspection or by computer-automated signal processing methods. While these methods are suitable for post-processing of collected ERP applications, they not well-suited for real-time processing of continuous ERP data. This project seeks to address the challenges associated with real-time identification of artifacts by introducing a machine learning model that can screen ERP, detect and reject artifact-corrupted data epochs prior to signal analysis. In addition to enabling real-time pre-processing of streaming ERP data, the DBScan machine-learning methods explored here can provide up to 90% accuracy in the identification of artifacts-mixed ERP epochs. As a result, the findings of this study will help to improve the signal quality of ERP trials and will enable ERP to be used as a biomarker in real-world applications where streaming EEG data collection and analysis are required.

Averaging amplitudes over consecutive time samples (i.e., time window) is widely used to calculate the peak amplitude of event-related potentials (ERPs). Cluster analysis of the spatio-temporal ERP data is a promising tool to determine the time window of an ERP of interest. However, determining an appropriate number of clusters to optimally represent ERPs is still challenging. Here, we develop a new method to estimate the optimal number of clusters utilizing consensus clustering. Various polarity dependent clustering methods, namely, k-means, hierarchical clustering, fuzzy c-means, self-organizing map, spectral clustering, and Gaussian mixture model, are used to configure consensus clustering after assessing them individually. When a range of clusters is applied many times, the optimal number of clusters should correspond to the expectation, which is the average of the obtained mean inner-similarities of estimated time windows across all conditions and groups converge in the satisfactory thresholds. In order to assess our method, the proposed method has been applied to simulated data and prospective memory experiment ERP data aimed to qualify N2 and P3, and N300 and prospective positivity components, respectively. The results of determining the optimal number of clusters meet at six cluster maps for both ERP data. In addition, our results revealed that the proposed method could be reliably applied to ERP data to determine the appropriate time window for the ERP of interest when the measurement interval is not accurately defined.

Time-frequency phase-synchrony approaches with ERPs

The current study examined the effects of variability on infant event-related potential (ERP) data editing methods. A widespread approach for analyzing infant ERPs is through a trial-by-trial editing process. Researchers identify electroencephalogram (EEG) channels containing artifacts and reject trials that are judged to contain excessive noise. This process can be performed manually by experienced researchers, partially automated by specialized software, or completely automated using an artifact-detection algorithm. Here, we compared the editing process from four different editors-three human experts and an automated algorithm-on the final ERP from an existing infant EEG dataset. Findings reveal that agreement between editors was low, for both the numbers of included trials and of interpolated channels. Critically, variability resulted in differences in the final ERP morphology and in the statistical results of the target ERP that each editor obtained. We also analyzed sources of disagreement by estimating the EEG characteristics that each human editor considered for accepting an ERP trial. In sum, our study reveals significant variability in ERP data editing pipelines, which has important consequences for the final ERP results. These findings represent an important step toward developing best practices for ERP editing methods in infancy research.

We reanalyzed, modeled and simulated Event-Related Potential (ERP) data from 13 healthy children (Mean age = 5.12, Standard Deviation = 0.75) during a computerized visual sustained target detection task. Extending an ERP-based ACT–R (Adaptive Control of Thought–Rational) neurocognitive modeling approach, we tested whether visual sustained selective-set attention in preschool children involves the enhancement of neural response to targets, and it shows key adult-like features (neurofunctional homology). Blinded automatic peaks analysis was conducted on vincentized binned grand ERP averages. Time-course and distribution of scalp activity were detailed through topographic mapping and paths analysis. Reaction times and accuracy were also measured. Adult Magnetic Resonance Imaging-based mapping using ACT–R dipole source modeling and electric-field spiking simulation provided very good fit with the actual ERP data (R2 > 0.70). In most electrodes, between 50 and 400 ms, ERPs concurrent with target presentation were enhanced relative to distractor, without manual response confounds. Triangulation of peak analysis, ACT–R modeling and simulation for the entire ERP epochs up to the moment of manual response (~700 ms, on average) suggested converging evidence of distinct but interacting processes of enhancement and planning for response release/inhibition, respectively. The latter involved functions and structures consistent with adult ERP activity which might correspond to a large-scale network, implicating Dorsal and Ventral Attentional Networks, corticostriatal loops, and subcortical hubs connected to prefrontal cortex top-down working memory executive control. Although preliminary, the present approach suggests novel directions for further tests and falsifiable hypotheses on the origins and development of visual selective attention and their ERP correlates.

Partial least squares analysis of neuroimaging data: applications and advances

Event-related potential (ERP) is the measurement of the brain's electrical activity in response to different types of events, such as attention, words, thinking, or sounds. By measuring the brain's response to such events, we can learn how different types of information are processed. As the mass of recorded ERP data explodes, an automatic and accurate tool to store, manage, and retrieve data readily is of increasing concern in neuroinformatics. In this paper, we describe a relational ERP database that has been constructed using the SQL server 2000 database management system and an IIS web server that has been setup for data retrieval through a custom web interface (http://202.113.232.103:8088/erpdb/index.asp). A novel database structure has been used to store ERP data of different activity channels, which provides a rapid and accurate way for data retrieval within any given range on the time zone with various searching options. The database is divided into: (1) subjects' information and record information and (2) ERP data, which has been structured and standardized in a database table supplemented with unrestricted text files. It can integrate or exchange data with other clinical databases or computer-based information systems through a program based on ADO techniques. Users are able to readily retrieve ERP data through the user-friendly web page interface. All online resources of the database are freely available to the scientific community. As the database develops further, we anticipate it will become a valuable tool that will make a great contribution to everyday clinical practice, teaching, and research work inneuroscience and psychology in the future.

Current analysis of event-related potentials (ERP) data is usually based on the a priori selection of channels and time windows of interest for studying the differences between experimental conditions in the spatio-temporal domain. In this work we put forward a new strategy designed for situations when there is not a priori information about 'when' and 'where' these differences appear in the spatio-temporal domain, simultaneously testing numerous hypotheses, which increase the risk of false positives. This issue is known as the problem of multiple comparisons and has been managed with methods that control the false discovery rate (FDR), such as permutation test and FDR methods. Although the former has been previously applied, to our knowledge, the FDR methods have not been introduced in the ERP data analysis. Here we compare the performance (on simulated and real data) of permutation test and two FDR methods (Benjamini and Hochberg (BH) and local-fdr, by Efron). All these methods have been shown to be valid for dealing with the problem of multiple comparisons in the ERP analysis, avoiding the ad hoc selection of channels and/or time windows. FDR methods are a good alternative to the common and computationally more expensive permutation test. The BH method for independent tests gave the best overall performance regarding the balance between type I and type II errors. The local-fdr method is preferable for high dimensional (multichannel) problems where most of the tests conform to the empirical null hypothesis. Differences among the methods according to assumptions, null distributions and dimensionality of the problem are also discussed.

The primary objective of this study was to further characterize the role of sex, if any, in event-related potentials (ERPs). More specifically, we aimed to investigate sex sensitivity with respect to the P300 ERP. Each female and male study participant underwent an oddball paradigm electroencephalography ERP session. ERP data were subjected to preprocessing and postprocessing, as well as statistical analysis. The results of the study showed that men had larger P300 amplitudes on average for both low-probability and high-probability stimuli compared with women (P<0.001). Men also showed shorter P300 latencies on average than did women (P<0.001). Significant differences in ERPs between men and women were observed in this study. The results indicate the existence of sex sensitivity to the P300 ERP, which may be because of men eliciting higher response inhibition compared with women.

Are cholinergic enhancers beneficial for memory in schizophrenia? An event-related potentials (ERPs) study of rivastigmine add-on therapy in a crossover trial