How Sensitive Are EEG Results to Preprocessing Methods: A Benchmarking Study.

Although several guidelines for best practices in EEG preprocessing have been released, even studies that strictly adhere to those guidelines contain considerable variation in the ways that the recommended methods are applied. An open question for researchers is how sensitive the results of EEG analyses are to variations in preprocessing methods and parameters. To address this issue, we analyze the effect of preprocessing methods on downstream EEG analysis using several simple signal and event-related measures.

Single-Trial Classification of Disfluent Brain States in Adults Who Stutter.

Normal human speech requires precise coordination between motor planning and sensory processing. Speech disfluencies are common when children learn to talk, but usually abate with time. About 5% of children experience stuttering.

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs).

BLASST: Band Limited Atomic Sampling With Spectral Tuning With Applications to Utility Line Noise Filtering.

Objective: In this paper, we present and test a new method for the identification and removal of nonstationary utility line noise from biomedical signals.

Methods: The method, band limited atomic sampling with spectral tuning (BLASST), is an iterative approach that is designed to 1) fit nonstationarities in line noise by searching for best-fit Gabor atoms at predetermined time points, 2) self-modulate its fit by leveraging information from frequencies surrounding the target frequency, and 3) terminate based on a convergence criterion obtained from the same surrounding frequencies. To evaluate the performance of the proposed algorithm, we generate several simulated and real instances of nonstationary line noise and test BLASST along with alternative filtering approaches.

Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG.

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.

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.

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 PREP pipeline: standardized preprocessing for large-scale EEG analysis.

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision.

MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB.

Experiments to monitor human brain activity during active behavior record a variety of modalities (e.g., EEG, eye tracking, motion capture, respiration monitoring) and capture a complex environmental context leading to large, event-rich time series datasets.

Detecting alpha spindle events in EEG time series using adaptive autoregressive models.

Background: Rhythmic oscillatory activity is widely observed during a variety of subject behaviors and is believed to play a central role in information processing and control. A classic example of rhythmic activity is alpha spindles, which consist of short (0.5-2 s) bursts of high frequency alpha activity.

A Framework for Content-based Retrieval of EEG with Applications to Neuroscience and Beyond.

This paper introduces a prototype framework for content-based EEG retrieval (CBER). Like content-based image retrieval, the proposed framework retrieves EEG segments similar to the query EEG segment in a large database. Such retrieval of EEG can be used to assist data mining of brain signals by allowing researchers to understand the association between brain patterns, responses, and the environment.

EEGVIS: A MATLAB Toolbox for Browsing, Exploring, and Viewing Large Datasets.

Recent advances in data monitoring and sensor technology have accelerated the acquisition of very large data sets. Streaming data sets from instrumentation such as multi-channel EEG recording usually must undergo substantial pre-processing and artifact removal. Even when using automated procedures, most scientists engage in laborious manual examination and processing to assure high quality data and to indentify interesting or problematic data segments.

SIDECACHE: Information access, management and dissemination framework for web services.

Background: Many bioinformatics algorithms and data sets are deployed using web services so that the results can be explored via the Internet and easily integrated into other tools and services. These services often include data from other sites that is accessed either dynamically or through file downloads. Developers of these services face several problems because of the dynamic nature of the information from the upstream services.

SIDEKICK: Genomic data driven analysis and decision-making framework.

Background: Scientists striving to unlock mysteries within complex biological systems face myriad barriers in effectively integrating available information to enhance their understanding. While experimental techniques and available data sources are rapidly evolving, useful information is dispersed across a variety of sources, and sources of the same information often do not use the same format or nomenclature. To harness these expanding resources, scientists need tools that bridge nomenclature differences and allow them to integrate, organize, and evaluate the quality of information without extensive computation.

Force feature spaces for visualization and classification.

Distance-preserving dimension reduction techniques can fail to separate elements of different classes when the neighborhood structure does not carry sufficient class information. We introduce a new visual technique, K-epsilon diagrams, to analyze dataset topological structure and to assess whether intra-class and inter-class neighborhoods can be distinguished.We propose a force feature space data transform that emphasizes similarities between same-class points and enhances class separability.

Propagating waves mediate information transfer in the motor cortex.

High-frequency oscillations in the beta range (10-45 Hz) are most active in motor cortex during motor preparation and are postulated to reflect the steady postural state or global attentive state of the animal. By simultaneously recording multiple local field potential signals across the primary motor and dorsal premotor cortices of monkeys (Macaca mulatta) trained to perform an instructed-delay reaching task, we found that these oscillations propagated as waves across the surface of the motor cortex along dominant spatial axes characteristic of the local circuitry of the motor cortex. Moreover, we found that information about the visual target to be reached was encoded in terms of both latency and amplitude of evoked waves at a time when the field phase-locked with respect to the target onset.

Extracting wave structure from biological data with application to responses in turtle visual cortex.

Waves have long been thought to be a fundamental mechanism for communicating information within a medium and are widely observed in biological systems. However, a quantitative analysis of biological waves is confounded by the variability and complexity of the response. This paper proposes a robust technique for extracting wave structure from experimental data by calculating "wave subspaces" from the KL decomposition of the data set.

Cellular pattern formation in circular domains.

An analysis of stationary and nonstationary cellular patterns observed in premixed flames on a circular, porous plug burner is presented. A phenomenological model is introduced, that exhibits patterns similar to the experimental states. The primary modes of the model are combinations of Fourier-Bessel functions, whose radial parts have neighboring zeros.

High-speed VSD imaging of visually evoked cortical waves: decomposition into intra- and intercortical wave motions.

In the pond turtle, Pseudemys scripta elegans, visually evoked cortical waves propagate at different velocities within the primary visual area compared with waves that pass into the secondary visual area. In an effort to separate intra- and intercortical wave motions, movies of visually evoked cortical waves recorded by high-speed voltage-sensitive dye (VSD) imaging were subjected to Karhunen-Loéve (KL) decomposition. This procedure decomposes the VSD movies into a series of basis images that capture different spatial patterns of coherent activity.