Hemodynamic correlates of spontaneous neural activity measured by human whole-head resting state EEG + fNIRS

Keleş, Hasan Onur; Barbour, Randall L.; Omurtag, Ahmet

doi:10.1016/j.neuroimage.2016.05.058

Cited by 42 publications

(43 citation statements)

References 84 publications

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“…We anticipate and hope that converging efforts in Hybrid hardware integration (Safaie et al, 2013) and data analysis (Biessmann et al, 2011; Keles et al, 2016), potentially based on detailed knowledge of underlying physiology (Bari et al, 2012; Mandrick et al, 2016a), will lead to more effective passive BMIs and other applications in neuroergonomics.…”

Section: Discussionmentioning

confidence: 99%

“…Each detector in NIRScout device records the signal from each separate source in two different wavelengths (760 and 850 nm). Oxy- and deoxyhemoglobin concentration changes (HbO and HbR) were computed using the modified Beer-Lambert law (Sassaroli and Fantini, 2004) using standard values for the chromophore extinction coefficients and differential path-length factor (Keles et al, 2016). fNIRS might be contaminated with the movement, heart rate, and Mayer wave artifacts.…”

Section: Methodsmentioning

confidence: 99%

“…The processed signals were inspected visually for the presence of muscle and motion, eye movements, and other artifacts. The recordings that were contaminated in excess of 10% by artifact were excluded as a whole (Keles et al, 2016). In addition, one subject was excluded since he was not sufficiently focused in the experiment according to 0-back low accuracy cut-off.…”

Section: Methodsmentioning

confidence: 99%

“…MWL detection using WM load as an experimental paradigm has been studied using EEG (Berka et al, 2007; Dornhege et al, 2007; Grimes et al, 2008; Brouwer et al, 2012), fNIRS (Hoshi et al, 2003; Izzetoglu et al, 2003; Ayaz et al, 2012; Durantin et al, 2014; Herff et al, 2014), and concurrent EEG and fNIRS (Hirshfield et al, 2009; Coffey et al, 2012). We have previously shown that aspects of NVC are characterizable by EEG+fNIRS, by taking advantage of the synergistic interaction between the modalities (Keles et al, 2016). The potential of EEG+fNIRS for active BCIs has recently been investigated (Fazli et al, 2012; Liu Y. et al, 2013; Tomita et al, 2014; Buccino et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Measuring Mental Workload with EEG+fNIRS

Aghajani

Garbey

Omurtag

2017

Front. Hum. Neurosci.

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167

115

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We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Measuring Mental Workload with EEG+fNIRS

Aghajani

Garbey

Omurtag

2017

Front. Hum. Neurosci.

Self Cite

167

115

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“…An average accuracy of 75.9% was achieved using an LDA-based classifier combining EEG, fNIRS, and ECG modalities. A new hybridization concept for combined EEG and fNIRS was introduced by Keles et al (2016). In their study, different EEG bands (α, β, θ, and Δ ) were estimated for the resting state.…”

Section: Hardware Combinationmentioning

confidence: 99%

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

2017

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In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

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