An EEG-based real-time cortical functional connectivity imaging system

Hwang, Han Jeong; Kim, Kyung Hwan; Jung, Young Jin; Kim, Do Won; Lee, Yong-Ho; Im, Chang-Hwan

doi:10.1007/s11517-011-0791-6

Cited by 12 publications

(4 citation statements)

References 55 publications

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“…Also, their tools were not integrated into a full online measurement setup including preprocessing and visualization. Hwang et al (2007Hwang et al ( , 2011 implemented a functional connectivity EEG pipeline based on source-level data, and the source estimation was realized with MNE. The functional connectivity metric was chosen to be a simple COR and included 12 nodes.…”

Section: Discussionmentioning

confidence: 99%

Online functional connectivity analysis of large all-to-all networks

Esch¹,

Dong²,

Hämäläinen³

et al. 2023

Preprint

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The analysis of EEG/MEG functional connectivity has become an important tool in neural research. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, i.e., after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved

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Section: Discussionmentioning

confidence: 99%

Online functional connectivity analysis of large all-to-all networks

Esch¹,

Dong²,

Hämäläinen³

et al. 2023

Preprint

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“…In particular, beta and gamma PSDs showed significantly lower mean error distances than those of delta, theta, and alpha PSDs. Much evidence has been accumulated indicating that a hand motor task significantly changes EEG frequency information in relatively higher frequency bands, i.e., alpha, beta, and gamma bands [ 28 – 31 , 38 – 41 ], and thus the higher performance obtained using higher frequency EEG features can be explained from a neurophysiological point of view. In particular, it has been relatively less documented that gamma frequency band is closely related to motor tasks as compared to alpha and beta frequency bands.…”

Section: Discussionmentioning

confidence: 99%

An artificial neural-network approach to identify motor hotspot for upper-limb based on electroencephalography: a proof-of-concept study

Choi

Han

Lee

et al. 2021

J NeuroEngineering Rehabil

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Background To apply transcranial electrical stimulation (tES) to the motor cortex, motor hotspots are generally identified using motor evoked potentials by transcranial magnetic stimulation (TMS). The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative to TMS. Methods EEG data were measured using 63 channels from thirty subjects as they performed a simple finger tapping task. Power spectral densities of the EEG data were extracted from six frequency bands (delta, theta, alpha, beta, gamma, and full) and were independently used to train and test an artificial neural network for motor hotspot identification. The 3D coordinate information of individual motor hotspots identified by TMS were quantitatively compared with those estimated by our EEG-based motor-hotspot-identification approach to assess its feasibility. Results The minimum mean error distance between the motor hotspot locations identified by TMS and our proposed motor-hotspot-identification approach was 0.22 ± 0.03 cm, demonstrating the proof-of-concept of our proposed EEG-based approach. A mean error distance of 1.32 ± 0.15 cm was measured when using only nine channels attached to the middle of the motor cortex, showing the possibility of practically using the proposed motor-hotspot-identification approach based on a relatively small number of EEG channels. Conclusion We demonstrated the feasibility of our novel EEG-based motor-hotspot-identification method. It is expected that our approach can be used as an alternative to TMS for motor hotspot identification. In particular, its usability would significantly increase when using a recently developed portable tES device integrated with an EEG device.

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“…Additionally, heterogeneity in the choice of neural markers used to construct the neurofeedback protocol constitutes a challenge. Most fMRI neurofeedback studies rely on average signals form single regions of interest (e.g., deCharms et al (119); Haller et al (168); Zotev et al (169); Berman et al (170); Garrison et al (171); Greer et al (150)), but several studies have used feedback from activation of whole networks (172, 173), or indices of connectivity between regions (174–177). So far, it is not known if and under which circumstances patients would benefit from training with one marker of neural activity compared to another.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Mental Imagery and Brain Regulation—New Links Between Psychotherapy and Neuroscience

Skottnik

Linden

2019

Front. Psychiatry

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Mental imagery is a promising tool and mechanism of psychological interventions, particularly for mood and anxiety disorders. In parallel developments, neuromodulation techniques have shown promise as add-on therapies in psychiatry, particularly non-invasive brain stimulation for depression. However, these techniques have not yet been combined in a systematic manner. One novel technology that may be able to achieve this is neurofeedback, which entails the self-regulation of activation in specific brain areas or networks (or the self-modulation of distributed activation patterns) by the patients themselves, through real-time feedback of brain activation (for example, from functional magnetic resonance imaging). One of the key mechanisms by which patients learn such self-regulation is mental imagery. Here, we will first review the main mental imagery approaches in psychotherapy and the implicated brain networks. We will then discuss how these networks can be targeted with neuromodulation (neurofeedback or non-invasive or invasive brain stimulation). We will review the clinical evidence for neurofeedback and discuss possible ways of enhancing it through systematic combination with psychological interventions, with a focus on depression, anxiety disorders, and addiction. The overarching aim of this perspective paper will be to open a debate on new ways of developing neuropsychotherapies.

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An EEG-based real-time cortical functional connectivity imaging system

Cited by 12 publications

References 55 publications

Online functional connectivity analysis of large all-to-all networks

Online functional connectivity analysis of large all-to-all networks

An artificial neural-network approach to identify motor hotspot for upper-limb based on electroencephalography: a proof-of-concept study

Mental Imagery and Brain Regulation—New Links Between Psychotherapy and Neuroscience

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