Multichannel wearable f<scp>NIRS‐EEG</scp>system for long‐term clinical monitoring

Kassab, Ali; Lan, Jérôme Le; Tremblay, Julie; Vannasing, Phetsamone; Dehbozorgi, Mahya; Pouliot, Philippe; Gallagher, Anne; Lesage, Frédéric; Sawan, Mohamad; Nguyen, Dang Khoa

doi:10.1002/hbm.23849

Cited by 60 publications

(41 citation statements)

References 103 publications

(105 reference statements)

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“…The technique has found widespread use both in the research and clinical field despite its low penetration depth and spatial resolution, as it provides good portability, safety, and ecological validity at low-cost and is therefore well-suited for both experimental and real-life settings Yücel et al, 2017). Similar to EEG, recent advances in fNIRS instrumentation have led to an increasing number of wearable, light weight, and fiberless systems (Scholkmann et al, 2014;von Lühmann et al, 2015;Zhao and Cooper, 2017) and wearable hybrid EEG-fNIRS systems (Safaie et al, 2013;von Lühmann et al, 2017;Kassab et al, 2018) that help translate BCI research from laboratory environments into real world applications.…”

Section: Introductionmentioning

confidence: 99%

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Lühmann

Ortega-Martínez

Boas

et al. 2020

Front. Hum. Neurosci.

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Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing-on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.

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Section: Introductionmentioning

confidence: 99%

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Lühmann

Ortega-Martínez

Boas

et al. 2020

Front. Hum. Neurosci.

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“…3 Continual fNIRS cerebral monitoring provides the ability to track regional oxygenation changes before, during, and after these ictal events. [4][5][6][7] In recent years, multimodal approaches have emerged integrating EEG with fNIRS to offer dual hemodynamic and electro-potential characterization of a seizure event, [8][9][10] whereas EEG data record the macroscopic temporal change in brain electrical activity; fNIRS approximates brain hemodynamic changes via spectroscopic measurements of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR). fNIRS depends on the slow dynamics of the hemodynamic response, thereby yielding lower temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

fNIRS improves seizure detection in multimodal EEG-fNIRS recordings

et al. 2019

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In the context of epilepsy monitoring, electroencephalography (EEG) remains the modality of choice. Functional near-infrared spectroscopy (fNIRS) is a relatively innovative modality that cannot only characterize hemodynamic profiles of seizures but also allow for long-term recordings. We employ deep learning methods to investigate the benefits of integrating fNIRS measures for seizure detection. We designed a deep recurrent neural network with long short-term memory units and subsequently validated it using the CHBMIT scalp EEG database-a compendium of 896 h of surface EEG seizure recordings. After validating our network using EEG, fNIRS, and multimodal data comprising a corpus of 89 seizures from 40 refractory epileptic patients was used as model input to evaluate the integration of fNIRS measures. Following heuristic hyperparameter optimization, multimodal EEG-fNIRS data provide superior performance metrics (sensitivity and specificity of 89.7% and 95.5%, respectively) in a seizure detection task, with low generalization errors and loss. False detection rates are generally low, with 11.8% and 5.6% for EEG and multimodal data, respectively. Employing multimodal neuroimaging, particularly EEG-fNIRS, in epileptic patients, can enhance seizure detection performance. Furthermore, the neural network model proposed and characterized herein offers a promising framework for future multimodal investigations in seizure detection and prediction.

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“…Ecological and long-term recordings of electrical and hemodynamic brain activity together with the evaluation of neurovascular coupling could allow diagnosis and monitoring of acute and chronic diseases that influence brain status and cerebrovascular health (e.g., epilepsy, stroke, vascular dementia, Alzheimer's Disease) [8,[64][65][66]. Such a neuroimaging tool can also be employed during intensive care and surgery [67][68][69][70][71][72]. Furthermore, the technology may be essential in rehabilitation protocols for studying brain plasticity and for implementing neurofeedback protocols based on brain computer interface approaches [73,74].…”

Section: Discussionmentioning

confidence: 99%

Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovascular Coupling

Chiarelli

Perpetuini

Croce

et al. 2020

Sensors

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Portable neuroimaging technologies can be employed for long-term monitoring of neurophysiological and neuropathological states. Functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are highly suited for such a purpose. Their multimodal integration allows the evaluation of hemodynamic and electrical brain activity together with neurovascular coupling. An innovative fNIRS-EEG system is here presented. The system integrated a novel continuous-wave fNIRS component and a modified commercial EEG device. fNIRS probing relied on fiberless technology based on light emitting diodes and silicon photomultipliers (SiPMs). SiPMs are sensitive semiconductor detectors, whose large detection area maximizes photon harvesting from the scalp and overcomes limitations of fiberless technology. To optimize the signal-to-noise ratio and avoid fNIRS-EEG interference, a digital lock-in was implemented for fNIRS signal acquisition. A benchtop characterization of the fNIRS component showed its high performances with a noise equivalent power below 1 pW. Moreover, the fNIRS-EEG device was tested in vivo during tasks stimulating visual, motor and pre-frontal cortices. Finally, the capabilities to perform ecological recordings were assessed in clinical settings on one Alzheimer’s Disease patient during long-lasting cognitive tests. The system can pave the way to portable technologies for accurate evaluation of multimodal brain activity, allowing their extensive employment in ecological environments and clinical practice.

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Multichannel wearable fNIRS‐EEGsystem for long‐term clinical monitoring

Cited by 60 publications

References 103 publications

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

fNIRS improves seizure detection in multimodal EEG-fNIRS recordings

Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovascular Coupling

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