Effect of data compression of ERP sign preprocessed by FWT algorithm upon a neural network classifier

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.

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.

Lack of repetition priming effect on visual event-related potentials in schizophrenia

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.

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

Multiresolution Analysis of Event-Related Potentials by Wavelet Decomposition

Early diagnosis of mild cognitive impairment and Alzheimer's disease using multimodal feature-based deep learning models in a Chinese elderly population.

Time effects on event-related brain potentials and vigilance performance

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

Impaired target detection in schizophrenia and the ventral attentional network: Findings from a joint event-related potential–functional MRI analysis

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.

Baddeley's influential model of working memory postulates a unitary central executive that allocates mental resources to several distinct short-term buffers. Subjects viewed individually presented single numerals and were required to maintain memory sets comprised of the most recently represented three stimuli. A clearly discernible visual event-related potentials (ERP) component emerged once the lengths of series of individual numbers exceeded memory set size and revision of working memory contents was required. An ERP correlate of working memory revision also emerged upon updating of auditory stimuli. This component was absent when subjects were exposed to the same series of stimuli in a standard “oddball” target detection situation. ERPs elicited when subjects were given the opportunity to rehearse without the need to update working memory contents clearly differed in latency from ERPs seen during updating. These findings provide support for previous studies suggesting a specific ERP correlate of central executive processes in working memory and are consistent with Baddeley's model.