In this software demonstration, we will show how formal ontologies can be used to label in-­‐ stances of neural (ERP) patterns that have been extracted from multiple datasets using a novel pipeline for pattern and metric extrac-­‐ tion (see Figure 1, next page). The entire dem-­‐ onstration will last ~15 minutes. We will begin with a 5-­‐minute introduction to ERP data from several cross-­‐laboratory studies of word com-­‐ prehension. This overview will motivate our demonstration by showing that ERP data are complex and heterogeneous, which explains the radical challenge of making valid compari-­‐ sons across different studies within our do-­‐ main. We will then give a 2-­‐minute description of the pipeline for analysis, which has two main components: (1) a set of pattern extraction (signal decomposition, temporal segmentation) methods; and (2) code to extract a variety of simple metrics (e.g., min and max intensity at a particular electrode) and to express these summary features as N-­‐triples, which are sub-­‐ sequently stored in RDF. Finally, we demon-­‐ strate how the NEMO ontology can be used to reason over these data. We highlight both ex-­‐ pected and novel findings for the test datasets and note that large-­‐scale application of this method could lead to major breakthroughs in understanding neurological patterns that are linked to sensory, motor, and cognitive pro-­‐ cesses in neurologically healthy and brain-­‐ injured children and adults. * To whom correspondence should be addressed: gfrishkoff@gsu.edu REFERENCES Liu, H., Frishkoff, G., Frank, R. M., & Dou, D. (2012, in press). Integration of Cognitive Neu- roscience Data: Metric and Pattern Matching across Heterogeneous ERP Datasets. Journal of Neurocomputing. Frishkoff, G., Frank, R., & LePendu, P. (2011). Ontology-based Analysis of Event-Related Po- tentials. Proceedings of the International Con- ference on Biomedical Ontology (ICBO'11). July 26-30, 2011. Buffalo, NY. Frishkoff, G., Frank, R., Sydes, J., Mueller, K., & Malony, A. (2011). Minimal Information for Neural Electromagnetic Ontologies (MI- NEMO): A standards-compliant workflow for analysis and integration of human EEG. Stan- dards in Genomic Sciences (SIGS), 5(2). Liu, H., Frishkoff, G.A., Frank, R., and Dou, D (2010). Ontology-based mining of brainwaves: sequence similarity technique for mapping al- ternative descriptions of patterns in event- related potentials (ERP) data. Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'10), 12 pages. Frishkoff, G.A., Dou, D., Frank, R., LePendu, P., and Liu, H. (2009). Development of Neural Electromagnetic Ontologies (NEMO): Repre- sentation and integration of event-related brain potentials. Proceedings of the International Con- ference on Biomedical Ontologies (ICBO09). July 24-26, 2009. Buffalo, NY. 2 Figure 1. [1] ERP pattern extraction. [2] Extraction of summary metrics. [3] Formatting of metrics in N-triples, with arguments defined by NEMO ontology classes and relations. [4] Capture of metadata about the experiment context (e.g., participants, measurement methods, experiment paradigm). [5] Interchange between NEMO ontology an NEMO ERP database.

In the present paper, we use data mining methods to address two challenges in the sharing and integration of data from electrophysiological (ERP) studies of human brain function. The first challenge, ERP metric matching, is to identify correspondences among distinct summary features ("metrics") in ERP datasets from different research labs. The second challenge, ERP pattern matching, is to align the ERP patterns or "components" in these datasets. We address both challenges within a unified framework. The utility of this framework is illustrated in a series of experiments using ERP datasets that are designed to simulate heterogeneities from three sources: (a) different groups of subjects with distinct simulated patterns of brain activity, (b) different measurement methods, i.e, alternative spatial and temporal metrics, and (c) different patterns, reflecting the use of alternative pattern analysis techniques. Unlike real ERP data, the simulated data are derived from known source patterns, providing a gold standard for evaluation of the proposed matching methods. Using this approach, we demonstrate that the proposed method outperforms well-known existing methods, because it utilizes cluster-based structure and thus achieves finer-grained representation of the multidimensional (spatial and temporal) attributes of ERP data.

We describe recent progress in the development and application of NEMO (Neural ElectroMagnetic Ontology), a formal ontology for the event-related potentials (ERP) domain. The ontology encodes knowledge about patterns that are commonly seen in ERP studies. The patterns are defined using equivalent class descriptions, which specify the spatial, temporal, and functional constraints that must be satisfied for an ERP instance, or datum, to belong to a particular pattern class. The data themselves are represented in RDF, using N-triples that link the data to the ontology. Our analysis pipeline automatically generates these RDF data. We then apply a reasoner, such as Hermit, to classify the data. By creating this pipeline, we have enabled our consortium partners to compare results across experiment paradigms using a common knowledge base and to refine that base (i.e., to add or adjust pattern descriptions) based on cross-lab study results. We discuss implications for ERP meta-anlaysis, discovery of new knowledge, and resolution of current controversies in the ERP literature.

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.

In this paper, we present a method for identifying correspondences, or mappings, between alternative features of brainwave activity in event-related potentials (ERP) data. The goal is to simulate mapping across results from heterogeneous methods that might be used in different neuroscience research labs. The input to the mapping consists of two ERP datasets whose spatiotemporal characteristics are captured by alternative sets of features, that is, summary spatial and temporal measures capturing distinct neural patterns that are linked to concepts in a set of ERP ontologies, called NEMO (Neural ElectroMagnetic Ontologies) [3, 6]. The feature value vector of each summary metric is transformed into a point-sequence curve, and clustering is performed to extract similar subsequences (clusters) representing the neural patterns that can then be aligned across datasets. Finally, the similarity between measures is derived by calculating the similarity between corresponding point-sequence curves. Experiment results showed that the proposed approach is robust and has achieved significant improvement on precision than previous algorithms.

Artifacts generated by biophysical sources (such as muscles, eyes, and heart) often hamper the use of EEG for the study of brain functions in basic research and applied settings. These artifacts share frequency overlap with the EEG, making frequency filtering inappropriate for their removal. Spatial decomposition methods, such as principal and independent components analysis, have been employed for the removal of the artifacts from the EEG. However, these methods have limitations that prevent their use in operational environments that require real-time analysis. We have introduced a directed components analysis (DCA) that employs a spatial template to direct the selection of target artifacts. This method is computationally efficient, allowing it to be employed in real-world applications. In this paper, we evaluate the effect of spatial undersampling of the scalp potential field on the ability of DCA to remove blink artifacts.

We describe a first-generation ontology forrepresentation and integration of event-related brain potentials (ERPs). The ontology is designed following OBO “best practices” and is augmented with tools to perform ontology-based labeling and annotation of ERP data, and a database that enables semantically based reasoning over these data. Because certain high-level concepts in the ERP domain are illdefined, we have developed methods to support coordinated updates to each of these three components. This approach consists of “top-down” (knowledge-driven) design and implementation, followed by “bottom-up” (data-driven) validation and refinement. Our goal is to build an ERP ontology that is logically valid, empirically sound, robust in application, and transparent to users. This ontology will be used to support sharing and meta-analysis of EEG and MEG data collected within our Neural Electromagnetic Ontologies (NEMO) project.

In this paper we describe the instrumentation and signal processing we have implemented as part of our bounded-EIT experiments performed with human subjects. Our hardware/software solution facilitates accurate and low-cost data collection for noninvasive conductivity estimation of human head tissues in vivo.

In the paper we describe a method to extract the topography of an impressed current for our bounded electrical impedance tomography (bEIT) studies. The frequency of the impressed current is low (up to a few hundred Hz), and is buried in background EEG and other noise. For the development of the extraction method, special consideration is given to maximize the signal-to-noise ratio. The standard lock-in detection framework, with its remarkable sensitivity at the locked-in frequency, is modified to suit our acquisition and post-processing environment. Simulation results are provided to showcase the accuracy and robustness of the modification in extracting both the amplitude and phase offset of the impressed signal in the face of different types of noise.

Modern neuroimaging technologies afford a non-invasive view into the functions of the human brain with great spatial (fMRI) and temporal resolution (EEG). However, common signal analytic methods require averaging over many trials, which limits the potential for practical application of these technologies. In this paper we advance a novel single-trial analysis method for EEG and demonstrate this approach with a target detection task. The method utilizes a framework consisting of multiple processing modules that can be applied in whole or in part, including noise mitigation, source-space transformation, discriminant analysis, and performance evaluation. The framework introduces an enhanced noise mitigation technology based on Directed Components Analysis (DCA) that improves upon existing spatial filtering techniques. Source-space transformation, utilizing a finite difference model (FDM) of the human head, estimates activity measures of the cortical sources involved in task performance. Such a source-space discrimination provides measurement invariance between training and testing sessions and holds the promise of providing a degree of classification not possible with scalp-recorded EEG. The framework's discrimination modules interface with performance evaluation modules to generate classification performance statistics. When applied to EEG acquired during performance of a target detection task, this method demonstrated that neural signatures of target recognition correctly classified up to 87% of targets in a rapid serial visual presentation (RSVP) of target/non-target images. On average, the single-trial classification method resulted in greater than 60% improvement over behavioral performance for target detection.

Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a generic framework for mining anddeveloping domain ontologies and apply it to mine brainwave (ERP) ontologies. The concepts and relationships in ERP ontologies can be mined according to the following steps: pattern decomposition, extraction of summary metrics for concept candidates, hierarchical clustering of patterns for classes and class taxonomies, and clustering-based classification and association rules mining for relationships (axioms) of concepts. We have applied this process to several dense-array (128-channel) ERP datasets. Results suggest good correspondence between mined concepts and rules, on the one hand, and patterns and rules that were independently formulated by domain experts, on the other. Data mining results also suggest ways in which expert-defined rules might be refined to improve ontologyrepresentation and classification results. The next goal of our ERP ontology mining framework is to address some long-standing challenges in conducting large-scale comparison and integration of results across ERP paradigms and laboratories. In a more general context, this work illustrates the promise of an interdisciplinary research program, which combines data mining, neuroinformatics andontology engineering to address real-world problems.

This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We describe recent progress on four goals: 1) specification of rules and concepts that capture expert knowledge of event-related potentials (ERP) patterns in visual word recognition; 2) implementation of rules in an automated data processing and labeling stream; 3) data mining techniques that lead to refinement of rules; and 4) iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods. As illustrated here, these methods are complementary and can lead to development of tools for pattern classification and labeling that are robust and conceptually transparent to researchers. The present application focuses on patterns in averaged EEG (ERP) data. We also describe efforts to extend our methods to represent patterns in MEG data, as well as EM patterns in source (anatomical) space. The broader aim of this work is to design an ontology-based system to support cross-laboratory, cross-paradigm, and cross-modal integration of brain functional data. Tools developed for this project are implemented in MATLAB and are freely available on request.

Objective: We present APECS (Automated Protocol for Evaluation of Electromagnetic Component Separation), a framework for evaluating the accuracy of blind source separation algorithms in removing artifacts from EEG data. APECS applies multiple, automated procedures to quantify the extent to which blinks are removed, and the degree to which nonocular activity is left intact.Methods: APECS was used to evaluate blink removal using three BSS algorithms: Second-Order Blind Inference (SOBI) and two Independent Component Analysis (ICA) implementations, FastICA and Infomax. The algorithms were applied to a series of blink-free EEG datasets, which were contaminated with real or simulated blinks. Extracted components were assumed to contain blink activity if correlation of their spatial projectors to a predefined blink template exceeded some threshold, and if polarity inverted above and below the eyes. Blink-related components were then subtracted to produce filtered data. The success of each data decomposition is evaluated through the use of multiple, automated metrics, to determine which decomposition best approximates the ideal solution (complete separation of blink from nonblink activity).Results: The outcomes for the evaluation measures were generally congruent, but also provided different and complementary information about the quality of each data decomposition. Under our testing framework, Infomax outperformed both FastICA and SOBI. Best results were achieved when blink activity loaded onto a single component.Conclusions: Multiple metrics, both quantitative and qualitative, are important in evaluating algorithms for artifact extraction.Significance: Failure to achieve complete separation of blink from nonblink activity can affect experimental outcomes, as illustrated here, using an ERP study of word-nonword discrimination. This illustrates the importance of methods for evaluation of artifact extraction results. (c) 2006 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

HiPerSAT, a C++ library and tools, processes EEG data sets with ICA (independent component analysis) methods. HiPerSAT uses BLAS, LAPACK, MPI and OpenMP to achieve a high performance solution that exploits parallel hardware. ICA is a class of methods for analyzing a large set of data samples and extracting independent components that explain the observed data. ICA is used in EEG research for data cleaning and separation of spatiotemporal patterns that may reflect different underlying neural processes. We present two ICA implementations (FastICA and Info-max) that exploit parallelism to provide an EEG component decomposition solution of higher performance and data capacity than current MATLAB-based implementations. Experimental results and the methodology used to obtain them are presented. Integrating HiPerSAT with EEGLAB (A. Delorme and S. Makeig, 2004) is described, as well as future plans for this research.

HiPerSAT, a C++ library and associated tools, processes large EEG data sets with statistical data whitening and ICA (Independent Component Analysis) methods. The library uses BLAS, LAPACK, MPI and OpenMP to achieve a high performance solution that exploits available parallel hardware. ICA is a class of methods for analyzing a large set of data samples and deducing the independent components responsible for the observed data. ICA is used in EEG signal analysis to understand neurological components of dynamic brain activity. We present two ICA implementations (FastICA and Infomax) that exploit parallelism to provide an EEG component decomposition solution of higher performance and data capacity than current MATLAB-based implementations. Experimental results and the methodology used to obtain them are presented. In addition, the integration of this functionality into the MATLAB-based EEGLAB tools [6] is described, as well as future plans for this research.

We present a method for evaluating ICA separation of artifacts from EEG (electroencephalographic) data. Two algorithms, Infomax and FastICA, were applied to “synthetic data,” created by superimposing simulated blinks on a blink-free EEG. To examine sensitivity to different data characteristics, multiple datasets were constructed by varying properties of the simulated blinks. ICA was used to decompose the data, and each source was cross- correlated with a blink template. Different thresholds for correlation were used to assess stability of the algorithms. When a match between the blink-template and a component was obtained, the contribution of the source was subtracted from the EEG. Since the original data were known a priori to be blink-free, it was possible to compute the correlation between these ”baseline” data and the results of different decompositions. By averaging the filtered data, time-locked to the simulated blinks, we illustrate effects of different outcomes for EEG waveform and topographic analysis.