Dataset Information

Detecting large-scale networks in the human brain using high-density electroencephalography.

ABSTRACT: High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc.

SUBMITTER: Liu Q

PROVIDER: S-EPMC6867042 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Detecting large-scale networks in the human brain using high-density electroencephalography.

Liu Quanying Q Farahibozorg Seyedehrezvan S Porcaro Camillo C Wenderoth Nicole N Mantini Dante D

Human brain mapping 20170620 9

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magn ...[more]

PMID: 28631281

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Project description:Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.

Dataset Information

Detecting large-scale networks in the human brain using high-density electroencephalography.

Publications

Detecting large-scale networks in the human brain using high-density electroencephalography.

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