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Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/eeglab), CTAGGER, is also available to speed the process of tagging existing studies.

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org).

A new paradigm for human brain imaging, mobile brain/body imaging (MoBI), involves synchronous collection of human brain activity (via electroencephalography, EEG) and behavior (via body motion capture, eye tracking, etc.), plus environmental events (scene and event recording) to study joint brain/body dynamics supporting natural human cognition supporting performance of naturally motivated human actions and interactions in 3-D environments (Makeig et al., 2009). Processing complex, concurrent, multi-modal, multi-rate data streams requires a signal-processing environment quite different from one designed to process single-modality time series data. Here we describe MoBILAB (more details available at sccn.ucsd.edu/wiki/MoBILAB), an open source, cross platform toolbox running on MATLAB (The Mathworks, Inc.) that supports analysis and visualization of any mixture of synchronously recorded brain, behavioral, and environmental time series plus time-marked event stream data. MoBILAB can serve as a pre-processing environment for adding behavioral and other event markers to EEG data for further processing, and/or as a development platform for expanded analysis of simultaneously recorded data streams.

UnlabelledThis paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data. A novel method that aims to identify single-trial event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better utilize the correlated ERP features. The method is applied to EEG recordings of a RSVP experiment with multiple sessions and subjects.The results show that the target image events are mainly characterized by 3 distinct patterns in the time-frequency domain, i.e., a theta band (4.3 Hz) power boosting 300-700 ms after the target image onset, an alpha band (12 Hz) power boosting 500-1000 ms after the stimulus onset, and a delta band (2 Hz) power boosting after 500 ms. The most discriminant time-frequency features are power boosting and are relatively consistent among multiple sessions and subjects.Since the original discriminant time-frequency features are highly correlated, we constructed the uncorrelated features using hierarchical clustering for better classification of target and non-target images. With feature clustering, performance (area under ROC) improved from 0.85 to 0.89 on within-session tests, and from 0.76 to 0.84 on cross-subject tests. The constructed uncorrelated features were more robust than the original discriminant features and corresponded to a number of local regions on the time-frequency plane.AvailabilityThe data and code are available at: http://compgenomics.cbi.utsa.edu/rsvp/index.html.

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments.

Human cognition has been shaped both by our body structure and by its complex interactions with its environment. Our cognition is thus inextricably linked to our own and others' motor behavior. To model brain activity associated with natural cognition, we propose recording the concurrent brain dynamics and body movements of human subjects performing normal actions. Here we tested the feasibility of such a mobile brain/body (MoBI) imaging approach by recording high-density electroencephalographic (EEG) activity and body movements of subjects standing or walking on a treadmill while performing a visual oddball response task. Independent component analysis of the EEG data revealed visual event-related potentials that during standing, slow walking, and fast walking did not differ across movement conditions, demonstrating the viability of recording brain activity accompanying cognitive processes during whole body movement. Non-invasive and relatively low-cost MoBI studies of normal, motivated actions might improve understanding of interactions between brain and body dynamics leading to more complete biological models of cognition.

Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain–computer interface (BCI) systems can make use of such knowledge to deliver relevant feedback to the user or to an observer, or within a human–machine system to increase safety and enhance overall performance. Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may in the future be increasingly ubiquitous. While performance of current BCI modeling methods is slowly increasing, current performance levels do not yet support widespread uses. Here we discuss the current neuroscientific questions and data processing challenges facing BCI designers and outline some promising current and future directions to address them. [ABSTRACT FROM PUBLISHER]

Introduction and objective: Independent component analysis (ICA) (Delorme and Makeig 2004) exploits the temporal resolution of EEG to provide a richer characterisation of macroscopic neural differences associated with ADHD. In this study, we investigate the inter-trial variability in EEG indices of ADHD. Methods: 68 twin pairs were recruited from the Twins Early Development Study (TEDS). EEG data was recorded during the fast task (Andreou et al. 2007). Using ICA, neural event-related activity was identified in both conditions of the fast task. Results: We observed that activities of the component processes varied with time and conditions in several frequency bands. Discussion: The use of ICA can reveal the dynamics of brain source activation and synchronization phenomena in ADHD not revealed by standard scalp data averaging methods. The increased variability of these parameters in ADHD suggests that there is not an attentional deficit, per se, in ADHD but an inability to maintain consistent performance over time. Conclusion: These findings will aid in the characterisation of intraindividual variability in ADHD, both at the level of cortical processing and at the level of behavior. [ABSTRACT FROM AUTHOR]

We suggest a solution to the following problem: "Given multichannel linear source mixture data Y, and an overcomplete dictionary, A, of source projections, ai, how can we construct a complete basis, A0, by selecting columns from A such that the sources X = A0(-1)Y are as statistically independent as possible from each other?". While conventional independent component analysis (ICA) methods learn the mixing matrix A0 from scratch given Y, we restrict ourselves to selecting basis vectors from a known overcomplete dictionary. We develop two methods based on modifications of the maximum likelihood equivalent of the Infomax approach and the reconstruction-ICA (RICA) algorithm. We show that on realistic synthetic electroencephalographic (EEG) data our algorithms can find the true sources in the case of a highly coherent dictionary while requiring relatively fewer data points compared to other algorithms. On real EEG data, our algorithms obtain higher mutual information reduction.

The traditional method of estimating an Event Related Potential (ERP) is to take the average of signal epochs time locked to a set of similar experimental events. This averaging method is useful as long as the experimental procedure can sufficiently isolate the brain or non-brain process of interest. However, if responses from multiple cognitive processes, time locked to multiple classes of closely spaced events, overlap in time with varying inter-event intervals, averaging will most likely fail to identify the individual response time courses. For this situation, we study estimation of responses to all recorded events in an experiment by a single model using standard linear regression (the rERP technique). Applied to data collected during a Rapid Serial Visual Presentation (RSVP) task, our analysis shows: (1) The rERP technique accounts for more variance in the data than averaging when individual event responses are highly overlapping; (2) the variance accounted for by the estimates is concentrated into a fewer ICA components than raw EEG channel signals.

Independent component analysis (ICA) can find distinct sources of electroencephalographic (EEG) activity, both brain-based and artifactual, and has become a common pre-preprocessing step in analysis of EEG data. Distinction between brain and non-brain independent components (ICs) accounting for, e.g., eye or muscle activities is an important step in the analysis. Here we present a fully automated method to identify eye-movement related EEG components by analyzing the spatial distribution of their scalp projections (scalp maps). The EyeCatch method compares each input scalp map to a database of eye-related IC scalp maps obtained by data-mining over half a million IC scalp maps obtained from 80,006 EEG datasets associated with a diverse set of EEG studies and paradigms. To our knowledge this is the largest sample of IC scalp maps that has ever been analyzed. Our result show comparable performance to a previous state-of-art semi-automated method, CORRMAP, while eliminating the need for human intervention.