Auditory enhancement of visual temporal order judgment

Abstract: Although numerous studies have shown that response times can be speeded by the presentation of multisensory stimuli, here we show that such speeding can be seen even when the second sensory channel fails to provide any task-relevant (i.e. redundant) information, and where cueing appears an unlikely explanation. Study participants performed a visual temporal order judgment task in the presence of task uninformative auditory cues, with the latter sound delayed relative to the latter visual cue. Responses were maximally speeded when the auditory stimulus was delayed by a short time (i.e. 100 ms) relative to the second visual target. These results illustrate a unique form of temporal benefit underlying a multisensory interaction, and form the basis for a novel explanation of these perceptual enhancements

Do Herbivores Eavesdrop on Ant Chemical Communication to Avoid Predation?

Strong effects of predator chemical cues on prey are common in aquatic and marine ecosystems, but are thought to be rare in terrestrial systems and specifically for arthropods. For ants, herbivores are hypothesized to eavesdrop on ant chemical communication and thereby avoid predation or confrontation. Here I tested the effect of ant chemical cues on herbivore choice and herbivory. Using Margaridisa sp. flea beetles and leaves from the host tree (Conostegia xalapensis), I performed paired-leaf choice feeding experiments. Coating leaves with crushed ant liquids (Azteca instabilis), exposing leaves to ant patrolling prior to choice tests (A. instabilis and Camponotus textor) and comparing leaves from trees with and without A. instabilis nests resulted in more herbivores and herbivory on control (no ant-treatment) relative to ant-treatment leaves. In contrast to A. instabilis and C. textor, leaves previously patrolled by Solenopsis geminata had no difference in beetle number and damage compared to control leaves. Altering the time A. instabilis patrolled treatment leaves prior to choice tests (0-, 5-, 30-, 90-, 180-min.) revealed treatment effects were only statistically significant after 90- and 180-min. of prior leaf exposure. This study suggests, for two ecologically important and taxonomically diverse genera (Azteca and Camponotus), ant chemical cues have important effects on herbivores and that these effects may be widespread across the ant family. It suggests that the effect of chemical cues on herbivores may only appear after substantial previous ant activity has occurred on plant tissues. Furthermore, it supports the hypothesis that herbivores use ant chemical communication to avoid predation or confrontation with ants

Open EEG Phantom

Welcome to the Open EEG Phantom project! OVERVIEW: The goal of this project is to provide freely available information for anyone interested in fabricating their own “phantom” for EEG and similar electrophysiology recording. Below are design files for 3-D printing (or whatever medium you prefer) mold and construction parts and related instructions for making your own EEG phantom. BACKGROUND: Why would we need an EEG phantom? For the same reason as any other biomedical imaging phantom – to provide a “ground truth” signal for use in validation, testing, and calibration of new data acquisition (DAQ) components. While MRI, PET, CT, and most other imaging modalities have well-established phantom methods, nothing has been adopted by the EEG development community. Classically, developers have used human subjects their test medium. Unfortunately this is not a good solution because of the lack of control of the underlying signal, or known for certain where the generating sources are, etc. The goal of this work is to provide a means to address this gap, freely available to the community. USES: The are a number of uses for such a device – basically, any case where you need a realistic, known-quantity signal. Examples: - Quantifying timing synchrony between devices in a complex DAQ setup (e.g. getting a timelocked signal into an EEG system along with other DAQ components) - Providing a training fixture for new technicians and lab members, for setting up and confirming the proper placement of electrodes without needing a human volunteer - Test fixture for calculating a true signal to noise ratio (SNR) of new electrodes or other DAQ components through input-output comparison with a realistic medium - Consistent, reliable medium for comparing signal quality of different electrodes or other DAQ components - Assessment tool for anticipated artifacts for a given environment or type of motion (e.g. placed in electrically noisy area, used with motion platform to recreate motion noise, etc) - Reliable model for re-creation of motion artifacts or other undesirable features, for testing efficacy of new de-noising or signal-extraction techniques MATERIALS: A full list of materials are included in each Components page, for each phantom type. Our intention is for this model to be easily fabricated without need for special tools or materials. Note – the proper materials used for the phantom “skin” in order to be a true replication of the human scalp is a very complex matter. At this moment, there is no ideal material that is easily fabricated. (However we are working diligently on new things!). At the moment, we suggest using ballistics gelatin, as it is the least expensive and easiest to work with that is “good enough” for many applications. As better materials are discovered this page will be updated…. * Initial work and design have been provided by W. David Hairston and Alfred Yu, at the Combat Capabilities Development Command Army Research Laboratory. As work created by U.S. Government employees, the files are considered to be in the public domain for copyright purposes in the United States of America. We're eager to see our products in use! Please send a picture of your phantom to [email protected] and [email protected] if it's been useful to you. This project also has a citeable Digital Object Identifier (DOI). Additionally, we welcome any feedback, comments, or anyone to join in the project of making phantoms for EEG applications. More information will be coming to this site soon

DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration

An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons

This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable benchmarking of EEG algorithms in many areas. The data was acquired using a Biosemi Active 2 EEG headset and includes 64 channels of EEG, 4 channels of EOG (electrooculogram), and 2 mastoid reference channels. Keywords: EEG, Visually evoked potential, EEGLAB, EEG study format (ESS), Hierarchical event descriptor (HED) tag

Task-related suppression of the brainstem frequency following response.

Recent evidence has shown top-down modulation of the brainstem frequency following response (FFR), generally in the form of signal enhancement from concurrent stimuli or from switching between attention-demanding task stimuli. However, it is also possible that the opposite may be true--the addition of a task, instead of a resting, passive state may suppress the FFR. Here we examined the influence of a subsequent task, and the relevance of the task modality, on signal clarity within the FFR. Participants performed visual and auditory discrimination tasks in the presence of an irrelevant background sound, as well as a baseline consisting of the same background stimuli in the absence of a task. FFR pitch strength and amplitude of the primary frequency response were assessed within non-task stimulus periods in order to examine influences due solely to general cognitive state, independent of stimulus-driven effects. Results show decreased signal clarity with the addition of a task, especially within the auditory modality. We additionally found consistent relationships between the extent of this suppressive effect and perceptual measures such as response time and proclivity towards one sensory modality. Together these results suggest that the current focus of attention can have a global, top-down effect on the quality of encoding early in the auditory pathway

EEG correlates of sensorimotor processing: independent components involved in sensory and motor processing

Melnik A, Hairston WD, Ferris DP, König P. EEG correlates of sensorimotor processing: independent components involved in sensory and motor processing. Scientific Reports. 2017;7(1): 4461

DETECT Function List and Summary.

<p>Additional information about the optional parameters and their default settings can be found by viewing the HTML function help pages located at <a href="http://visual.cs.utsa.edu/detect/documentation/help/" target="_blank">http://visual.cs.utsa.edu/detect/documentation/help/</a>. Additional information can also be found in the DETECT Users Guide, provided with the toolbox.</p

A segment of dataset D2 with regions labeled from a user (A) and the automated labeling from DETECT (B).

<p>A segment of dataset D2 with regions labeled from a user (A) and the automated labeling from DETECT (B).</p