Dataset Information

HD-EEG for tracking sub-second brain dynamics during cognitive tasks.

ABSTRACT: This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and their rapid reconfigurations at sub-second time scale in different conditions, (naming/spelling/rest) and modalities, (auditory/visual) and compare them to each other, (ii) validate several parameters involved in the methods used to estimate cortical brain networks through scalp EEG, such as the open question of optimal number of channels and number of regions of interest and (iii) allow the reproducibility of results obtained so far using HD-EEG. We hope that delivering these datasets will lead to the development of new methods that can be used to estimate brain cortical networks and to better understand the general functioning of the brain during rest and task. Data are freely available from https://openneuro.org .

SUBMITTER: Mheich A

PROVIDER: S-EPMC7840668 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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HD-EEG for tracking sub-second brain dynamics during cognitive tasks.

Mheich A A Dufor O O Yassine S S Kabbara A A Biraben A A Wendling F F Hassan M M

Scientific data 20210127 1

This work provides the community with high-density Electroencephalography (HD-EEG, 256 channels) datasets collected during task-free and task-related paradigms. It includes forty-three healthy participants performing visual naming and spelling tasks, visual and auditory naming tasks and a visual working memory task in addition to resting state. The HD-EEG data are furnished in the Brain Imaging Data Structure (BIDS) format. These datasets can be used to (i) track brain networks dynamics and thei ...[more]

PMID: 33504796

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Project description:ObjectivesListening to speech in adverse listening conditions is effortful. Objective assessment of cognitive spare capacity during listening can serve as an index of the effort needed to understand speech. Cognitive spare capacity is influenced both by signal-driven demands posed by listening conditions and top-down demands intrinsic to spoken language processing, such as memory use and semantic processing. Previous research indicates that electrophysiological responses, particularly alpha oscillatory power, may index listening effort. However, it is not known how these indices respond to memory and semantic processing demands during spoken language processing in adverse listening conditions. The aim of the present study was twofold: first, to assess the impact of memory demands on electrophysiological responses during recognition of degraded, spoken sentences, and second, to examine whether predictable sentence contexts increase or decrease cognitive spare capacity during listening.DesignCognitive demand was varied in a memory load task in which young adult participants (n = 20) viewed either low-load (one digit) or high-load (seven digits) sequences of digits, then listened to noise-vocoded spoken sentences that were either predictable or unpredictable, and then reported the final word of the sentence and the digits. Alpha oscillations in the frequency domain and event-related potentials in the time domain of the electrophysiological data were analyzed, as was behavioral accuracy for both words and digits.ResultsMeasured during sentence processing, event-related desynchronization of alpha power was greater (more negative) under high load than low load and was also greater for unpredictable than predictable sentences. A complementary pattern was observed for the P300/late positive complex (LPC) to sentence-final words, such that P300/LPC amplitude was reduced under high load compared with low load and for unpredictable compared with predictable sentences. Both words and digits were identified more quickly and accurately on trials in which spoken sentences were predictable.ConclusionsResults indicate that during a sentence-recognition task, both cognitive load and sentence predictability modulate electrophysiological indices of cognitive spare capacity, namely alpha oscillatory power and P300/LPC amplitude. Both electrophysiological and behavioral results indicate that a predictive sentence context reduces cognitive demands during listening. Findings contribute to a growing literature on objective measures of cognitive demand during listening and indicate predictable sentence context as a top-down factor that can support ease of listening.

Project description:Brain activity is composed of oscillatory and broadband arrhythmic components; however, there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal dynamics of broadband arrhythmic electroencephalography (EEG) remain unexplored. We have previously demonstrated that broadband arrhythmic EEG contains both short- and long-range temporal correlations that change significantly during movement. In this study, we build upon our previous work to gain a deeper understanding of these changes in the long-range temporal correlation (LRTC) in broadband EEG and contrast them with the well-known LRTC in alpha oscillation amplitude typically found in the literature. We investigate and validate changes in LRTCs during five different types of movements and motor imagery tasks using two independent EEG datasets recorded with two different paradigms-our finger tapping dataset with single self-initiated asynchronous finger taps and publicly available EEG dataset containing cued continuous movement and motor imagery of fists and feet. We quantified instantaneous changes in broadband LRTCs by detrended fluctuation analysis on single trial 2 s EEG sliding windows. The broadband LRTC increased significantly (p < 0.05) during all motor tasks as compared to the resting state. In contrast, the alpha oscillation LRTC, which had to be computed on longer stitched EEG segments, decreased significantly (p < 0.05) consistently with the literature. This suggests the complementarity of underlying fast and slow neuronal scale-free dynamics during movement and motor imagery. The single trial broadband LRTC gave high average binary classification accuracy in the range of 70.54±10.03% to 76.07±6.40% for all motor execution and imagery tasks and hence can be used in brain-computer interface (BCI). Thus, we demonstrate generalizability, robustness, and reproducibility of novel motor neural correlate, the single trial broadband LRTC, during different motor execution and imagery tasks in single asynchronous and cued continuous motor-BCI paradigms and its contrasting behavior with LRTC in alpha oscillation amplitude.

Dataset Information

HD-EEG for tracking sub-second brain dynamics during cognitive tasks.

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HD-EEG for tracking sub-second brain dynamics during cognitive tasks.

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