Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset

Shin, Jaeyoung; Lühmann, Alexander von; Kim, Do Won; Mehnert, Jan; Hwang, Han Jeong; Müller, Klaus Robert

doi:10.1038/sdata.2018.3

Cited by 135 publications

(137 citation statements)

References 56 publications

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“…As the 0-back task went on, the HbO 2 concentration decreased due to the low cognitive load. These activation results are consistent with previous n-back studies with NIRS 35,36 and demonstrate WM-related frontal activation.…”

Section: Discussionsupporting

confidence: 93%

Connectivity properties in the prefrontal cortex during working memory: a near-infrared spectroscopy study

et al. 2019

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Working memory (WM) plays a crucial role in human brain functions. The application of brain connectivity analysis helps to understand the brain network properties in WM. Combination of functional and effective connectivity can provide new insights for exploring network attributes. Nevertheless, few studies have combined these two modes in WM. Near-infrared spectroscopy was used to investigate the connectivity properties in the prefrontal cortex (PFC) during n-back (0-back and 2-back) tasks by combining functional and effective connectivity analysis. Our results demonstrated that the PFC network showed small-world properties in both WM tasks. The characteristic path length was significantly longer in the 2-back task than in the 0-back task, while there was no obvious difference in the clustering coefficient between two tasks. Regarding the effective connectivity, the Granger causality (GC) was higher for right PFC→left PFC than for left PFC→right PFC in the 2-back task. Compared with the 0-back task, GC of right PFC→left PFC was higher in the 2-back task. Our findings show that, along with memory load increase, long range connections in PFC are enhanced and this enhancement might be associated with the stronger information flow from right PFC to left PFC. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Discussionsupporting

confidence: 93%

Connectivity properties in the prefrontal cortex during working memory: a near-infrared spectroscopy study

et al. 2019

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“…Our study aimed to explore the complex mechanisms of interaction between motor and pain, which have not been yet clearly understood. The co-recording of EEG and functional Near-Infrared Spectroscopy (fNIRS) has been demonstrated to be a very promising technique to explore both electrical and metabolic activities [17] during multimodal stimulations condition. In our preliminary study [18] we adopted a concomitant recording fNIRS and laser evoked potentials (LEPS) to explore the complex mutual interference between motor cortex activation and the processing of painful stimuli in FM patients and healthy subjects.…”

Section: Introductionmentioning

confidence: 99%

Mutual interaction between motor cortex activation and pain in fibromyalgia: EEG-fNIRS study

et al. 2020

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Background Experimental and clinical studies suggested an analgesic effect on chronic pain by motor cortex activation. The present study explored the complex mechanisms of interaction between motor and pain during performing the slow and fast finger tapping task alone and in concomitant with nociceptive laser stimulation. Method The participants were 38 patients with fibromyalgia (FM) and 21 healthy subjects. We used a simultaneous multimodal method of laser-evoked potentials and functional near-infrared spectroscopy to investigate metabolic and electrical changes during the finger tapping task and concomitant noxious laser stimulation. Functional near-infrared spectroscopy is a portable and optical method to detect cortical metabolic changes. Laser-evoked potentials are a suitable tool to study the nociceptive pathways function. Results We found a reduced tone of cortical motor areas in patients with FM compared to controls, especially during the fast finger tapping task. FM patients presented a slow motor performance in all the experimental conditions, requesting rapid movements. The amplitude of laser evoked potentials was different between patients and controls, in each experimental condition, as patients showed smaller evoked responses compared to controls. Concurrent phasic pain stimulation had a low effect on motor cortex metabolism in both groups nor motor activity changed laser evoked responses in a relevant way. There were no correlations between Functional Near-Infrared Spectroscopy (FNIRS) and clinical features in FM patients.

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“…We employed four different NIRS datasets recorded by the first author of this paper. Datasets denoted by "dataset I" and "dataset II" can be freely downloaded at: http://doc.ml.tu-berlin.de/hBCI/ (Shin et al, 2017b) and "dataset III" can be downloaded at: http://dx.doi.org/10.14279/depositonce-5830 (Shin et al, 2018c). "Dataset IV" is a NIRS dataset used in the study of Shin et al (2018b).…”

Section: Methodsmentioning

confidence: 99%

“…Location of the NIRS channels for (A) datasets I-II, (B) dataset III, and (C) dataset IV. These figures were amended fromShin et al (2017b;2018b;2018c). Blue and red crosses are the specific positions of source and detector, respectively.…”

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confidence: 99%

Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating

Shin

2020

Front. Neurosci.

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Ensemble classifiers have been proven to result in better classification accuracy than that of a single strong learner in many machine learning studies. Although many studies on electroencephalography-brain-computer interface (BCI) used ensemble classifiers to enhance the BCI performance, ensemble classifiers have hardly been employed for near-infrared spectroscopy (NIRS)-BCIs. In addition, since there has not been any systematic and comparative study, the efficacy of ensemble classifiers for NIRS-BCIs remains unknown. In this study, four NIRS-BCI datasets were employed to evaluate the efficacy of linear discriminant analysis ensemble classifiers based on the bootstrap aggregating. From the analysis results, significant (or marginally significant) increases in the bitrate as well as the classification accuracy were found for all four NIRS-BCI datasets employed in this study. Moreover, significant bitrate improvements were found in two of the four datasets.

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Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset

Cited by 135 publications

References 56 publications

Connectivity properties in the prefrontal cortex during working memory: a near-infrared spectroscopy study

Connectivity properties in the prefrontal cortex during working memory: a near-infrared spectroscopy study

Mutual interaction between motor cortex activation and pain in fibromyalgia: EEG-fNIRS study

Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating

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