fNIRS improves seizure detection in multimodal EEG-fNIRS recordings

Sirpal, Parikshat; Kassab, Ali; Pouliot, Philippe; Nguyen, Dang Khoa; Lesage, Frédéric

doi:10.1117/1.jbo.24.5.051408

Cited by 52 publications

(36 citation statements)

References 33 publications

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“…In addition to this, there are several fNIRS based ML studies that focuses on classification of several psychiatric disorders also uses ΔHb (Cheng et al, 2019; Crippa et al, 2017; Hernandez-Meza et al, 2018; Hernandez-Meza et al, 2017; J. Li et al, 2016; Rosas-Romero et al, 2019; Sirpal et al, 2019; Sutoko et al, 2019) and for some cases ΔHb based features might also show higher accuracy results compared to ΔHbO based ones (Crippa et al, 2017). After estimating the CC based FC matrix, it was normalized by performing Fisher’s z-transformation due to reduce skewness.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Flourishing Levels of Individuals by Using Resting State fNIRS with Different Functional Connectivity Measures

Eken

2021

Preprint

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Flourishing is an important criterion to assess wellbeing, however, controversies remain, particularly around assessing it with self-report measures. Due to this reason, to be able to understand the underlying neural mechanisms of well-being, researchers often utilize neuroimaging techniques. However, rather than individual answers, previous neuroimaging studies using statistical approaches provided an answer in average sense. To overcome these problems, we applied machine learning techniques to discriminate 43 highly flourishing from regular flourishing individuals by using a publicly available resting state functional near infrared spectroscopy (rs-fNIRS) dataset to get an answer in individual level. We utilized both Pearson’s correlation (CC) and Dynamic Time Warping (DTW) algorithm to estimate functional connectivity from rs-fNIRS data on temporo-parieto-occipital region as input to nine different machine learning algorithms. Our results revealed that by utilizing oxyhemoglobin concentration change with Pearson’s correlation (CC – ΔHbO) and deoxy hemoglobin concentration change with dynamic time warping (DTW – ΔHb), we could be able to classify flourishing individuals with 90 % accuracy with AUC 0.90 and 0.93 using nearest neighbor and Radial Basis Kernel Support Vector Machine. This finding suggests that temporo-parieto-occipital regional based resting state connectivity might be a potential biomarker to identify the levels of flourishing and using both connectivity measures might allow us to find different potential biomarkers.

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Section: Methodsmentioning

confidence: 99%

Assessment of Flourishing Levels of Individuals by Using Resting State fNIRS with Different Functional Connectivity Measures

Eken

2021

Preprint

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“…Chiarelli et al ( 2018 ) found a significant increase in classification accuracy for multimodel EEG-fNIRS recording than standalone EEG and fNIRS signals and other classification algorithms. Sirpal et al ( 2019 ) proposed a deep recurrent neural network for seizure detection in multimodel EEG-fNIRS recording and found that this promising framework can be used in future EEG-fNIRS models to make detection and prediction.…”

Section: Hybrid Eeg-fnirs-based Bcimentioning

confidence: 99%

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Khan

Naseer

Yazidi

et al. 2021

Front. Hum. Neurosci.

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Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

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“…103 A model based on long short-term memory in the recurrent neural networks demonstrated an e±cient performance in seizure detection in a hybrid EEG-fNIRS study. 104…”

Section: Resting Statementioning

confidence: 99%

“…Few studies could examine most of the brain regions. 85,91,104,108,229 Neuronal activations occurring in response to a single task are not linked to a single brain location. 351 Therefore, the outcomes of the studies that focus on a speci¯c narrow location in the brain might not be su±cient for understanding the brain functions.…”

Section: Channel Resolution and Limitationmentioning

confidence: 99%

Application of functional near-infrared spectroscopy in the healthcare industry: A review

Hong

Yaqub

2019

J. Innov. Opt. Health Sci.

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Functional near-infrared spectroscopy (fNIRS), a growing neuroimaging modality, has been utilized over the past few decades to understand the neuronal behavior in the brain. The technique has been used to assess the brain hemodynamics of impaired cohorts as well as able-bodied. Neuroimaging is a critical technique for patients with impaired cognitive or motor behaviors. The portable nature of the fNIRS system is suitable for frequent monitoring of the patients who exhibit impaired brain activity. This study comprehensively reviews brain-impaired patients: The studies involving patient populations and the diseases discussed in more than 10 works are included. Eleven diseases examined in this paper include autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, depressive disorders, anxiety and panic disorder, schizophrenia, mild cognitive impairment, Alzheimer’s disease, Parkinson’s disease, stroke, and traumatic brain injury. For each disease, the tasks used for examination, fNIRS variables, and significant findings on the impairment are discussed. The channel configurations and the regions of interest are also outlined. Detecting the occurrence of symptoms at an earlier stage is vital for better rehabilitation and faster recovery. This paper illustrates the usability of fNIRS for early detection of impairment and the usefulness in monitoring the rehabilitation process. Finally, the limitations of the current fNIRS systems (i.e., nonexistence of a standard method and the lack of well-established features for classification) and future research directions are discussed. The authors hope that the findings in this paper would lead to advanced breakthrough discoveries in the fNIRS field in the future.

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fNIRS improves seizure detection in multimodal EEG-fNIRS recordings

Cited by 52 publications

References 33 publications

Assessment of Flourishing Levels of Individuals by Using Resting State fNIRS with Different Functional Connectivity Measures

Assessment of Flourishing Levels of Individuals by Using Resting State fNIRS with Different Functional Connectivity Measures

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Application of functional near-infrared spectroscopy in the healthcare industry: A review

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