Capturing the nature of events and event context using Hierarchical Event Descriptors (HED)

Because of the central role that event-related data analysis plays in EEG and MEG (MEEG) experiments, choices about which events to report and how to annotate their full natures can significantly influence the reliability, reproducibility, and value of MEEG datasets for further analysis. Current, more powerful annotation strategies combine 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 dissects the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson (openneuro.org, ds000117) shared by its authors using Brain Imaging Data Structure (BIDS) formatting (bids.neuroimaging.io). We propose a set of best practices and guidelines for event handling in MEEG research, examine the impact of various design decisions, and provide a working template for organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using the new third-generation formulation of the Hierarchical Event Descriptors (HED-3G) framework and tools (hedtags.org) can document events occurring during neuroimaging experiments and their interrelationships, providing machine-actionable annotation enabling automated both within- and across-study comparisons and analysis, and point to a more complete BIDS formatted, HED-3G annotated edition of the MEEG portion of the Wakeman and Henson dataset (OpenNeuro ds003645).

Towards retrieving social events from spatio-temporal photo annotation

An event can be defined as a happening that gathers people with some common goal over a period of time and in a certain place. This paper presents a new method to retrieve social events through annotations in spatio-temporal photo collections, known as STEve-PR (Spatio-Temporal EVEnt Photo Retrieval). The proposed technique uses a clustering algorithm to gather similar photos by considering the location, date and time of the photos. The STEve-PR clustering approach clusters photos belonging to the same event. STEve-PR uses spatial clusters created to propagate event annotation between photos in the same cluster and employs TF-IDF similarity between tags to find the spatial cluster with the highest similarity for photos without a geographical location. We evaluated our approach on a public database.

Automated EEG mega-analysis II: Cognitive aspects of event related features

In this paper, we present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings containing approximately 7.8 million event instances acquired under several different experimental paradigms. Such large-scale analysis is predicated on consistent data organization and event annotation as well as an effective automated pre-processing pipeline to transform raw EEG into a form suitable for comparative analysis. A key component of this analysis is the annotation of study-specific event codes using a common vocabulary to describe relevant event features. We demonstrate that Hierarchical Event Descriptors (HED tags) capture statistically significant cognitive aspects of EEG events common across multiple recordings, subjects, studies, paradigms, headset configurations, and experimental sites. We use representational similarity analysis (RSA) to show that EEG responses annotated with the same cognitive aspect are significantly more similar than those that do not share that cognitive aspect. These RSA similarity results are supported by visualizations that exploit the non-linear similarities of these associations. We apply temporal overlap regression to reduce confounds caused by adjacent events instances and extract time and time-frequency EEG features (regressed ERPs and ERSPs) that are comparable across studies and replicate findings from prior, individual studies. Likewise, we use second-level linear regression to separate effects of different cognitive aspects on these features, across all studies. This work demonstrates that EEG mega-analysis (pooling of raw data across studies) can enable investigations of brain dynamics in a more generalized fashion than single studies afford. A companion paper complements this event-based analysis by addressing commonality of the time and frequency statistical properties of EEG across studies at the channel and dipole level.

Towards an event annotated corpus of Polish

Towards an event annotated corpus of PolishThe paper presents a typology of events built on the basis of TimeML specification adapted to Polish language. Some changes were introduced to the definition of the event categories and a motivation for event categorization was formulated. The event annotation task is presented on two levels – ontology level (language independent) and text mentions (language dependant). The various types of event mentions in Polish text are discussed. A procedure for annotation of event mentions in Polish texts is presented and evaluated. In the evaluation a randomly selected set of documents from the Corpus of Wrocław University of Technology (called KPWr) was annotated by two linguists and the annotator agreement was calculated. The evaluation was done in two iterations. After the first evaluation we revised and improved the annotation procedure. The second evaluation showed a significant improvement of the agreement between annotators. The current work was focused on annotation and categorisation of event mentions in text. The future work will be focused on description of event with a set of attributes, arguments and relations.