Deep Learning in Physiological Signal Data: A Survey

Sensors, Journal Year: 2020, Volume and Issue: 20(4), P. 969 - 969

Published: Feb. 11, 2020

Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data the form of 1D signals have yet to be beneficially exploited from this novel fulfil desired tasks. Therefore, paper we survey latest scientific research on deep learning signal such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), electrooculogram (EOG). We found 147 papers published between January 2018 October 2019 inclusive various journals publishers. The objective is conduct detailed study comprehend, categorize, compare key parameters deep-learning approaches that been used analysis applications. review are input type, task, model, training architecture, dataset sources. Those main affect system performance. taxonomize works using method based on: (1) perspective, modality application; (2) concept perspective architecture

Language: Английский

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Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review

Sensors, Journal Year: 2022, Volume and Issue: 22(15), P. 5865 - 5865

Published: Aug. 5, 2022

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better resolution, though it is constrained by its One important merit shared the that both modalities have favorable portability could be integrated into compatible experimental setup, providing compelling ground development of multimodal fNIRS-EEG integration analysis approach. Despite growing number studies using concurrent designs reported in recent years, methodological reference past remains unclear. To fill this knowledge gap, review critically summarizes status methods currently used studies, an up-to-date overview guideline future projects to conduct studies. A literature search was conducted PubMed Web Science through 31 August 2021. After screening qualification assessment, 92 involving data recordings analyses were included final review. Specifically, three categories analyses, including EEG-informed fNIRS-informed parallel identified explained with detailed description. Finally, we highlighted current challenges potential directions research.

Language: Английский

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Deep learning in fNIRS: a review

Neurophotonics, Journal Year: 2022, Volume and Issue: 9(04)

Published: July 20, 2022

Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional near-infrared spectroscopy (fNIRS) studies depend heavily on data processing pipeline classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast accurate performances tasks across many biomedical fields. Aim: We aim review emerging DL applications fNIRS studies. Approach: first introduce some commonly used techniques. Then, summarizes current work most active areas this field, including brain-computer interface, neuro-impairment diagnosis, neuroscience discovery. Results: Of 63 papers considered review, 32 report comparative study techniques traditional machine where 26 been shown outperforming latter terms accuracy. In addition, eight also utilize reduce amount preprocessing typically done with or increase via augmentation. Conclusions: The application mitigate hurdles present such as lengthy small sample sizes while achieving comparable improved

Language: Английский

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A systematic review on hybrid EEG/fNIRS in brain-computer interface

Biomedical Signal Processing and Control, Journal Year: 2021, Volume and Issue: 68, P. 102595 - 102595

Published: April 2, 2021

Language: Английский

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Wearable, Integrated EEG–fNIRS Technologies: A Review

Sensors, Journal Year: 2021, Volume and Issue: 21(18), P. 6106 - 6106

Published: Sept. 12, 2021

There has been considerable interest in applying electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) simultaneously for multimodal assessment of brain function. EEG–fNIRS can provide a comprehensive picture electrical hemodynamic function applied across various fields science. The development wearable, mechanically electrically integrated technology is critical next step the evolution this field. A suitable system design could significantly increase data/image quality, wearability, patient/subject comfort, capability long-term monitoring. Here, we present concise, yet comprehensive, review progress that made toward achieving system. Significant marks include both discrete component-based microchip-based technologies; modular systems; miniaturized, lightweight form factors; wireless capabilities; shared analogue-to-digital converter (ADC) architecture between fNIRS EEG data acquisitions. In describing attributes, advantages, disadvantages current technologies, aims to roadmap generation systems.

Language: Английский

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

Frontiers in Human Neuroscience, Journal Year: 2021, Volume and Issue: 14

Published: Jan. 25, 2021

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

Language: Английский

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An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 149, P. 106053 - 106053

Published: Sept. 1, 2022

Language: Английский

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EF-Net: Mental State Recognition by Analyzing Multimodal EEG-fNIRS via CNN

Sensors, Journal Year: 2024, Volume and Issue: 24(6), P. 1889 - 1889

Published: March 15, 2024

Analysis of brain signals is essential to the study mental states and various neurological conditions. The two most prevalent noninvasive for measuring activities are electroencephalography (EEG) functional near-infrared spectroscopy (fNIRS). EEG, characterized by its higher sampling frequency, captures more temporal features, while fNIRS, with a greater number channels, provides richer spatial information. Although few previous studies have explored use multimodal deep-learning models analyze activity both EEG subject-independent training–testing split analysis remains underexplored. results setting directly show model’s ability on unseen subjects, which crucial real-world applications. In this paper, we introduce EF-Net, new CNN-based model. We evaluate EF-Net an EEG-fNIRS word generation (WG) dataset state recognition task, primarily focusing setting. For completeness, report in subject-dependent subject-semidependent settings as well. compare our model five baseline approaches, including three traditional machine learning methods deep methods. demonstrates superior performance accuracy F1 score, surpassing these baselines. Our achieves scores 99.36%, 98.31%, 65.05% subject-dependent, subject-semidependent, settings, respectively, best 1.83%, 4.34%, 2.13% These highlight EF-Net’s capability effectively learn interpret across different subjects.

Language: Английский

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

Journal of Innovative Optical Health Sciences, Journal Year: 2019, Volume and Issue: 12(06)

Published: Sept. 12, 2019

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

Language: Английский

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Hybrid EEG-fNIRS Brain Computer Interface Based on Common Spatial Pattern by Using EEG-Informed General Linear Model

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2023, Volume and Issue: 72, P. 1 - 10

Published: Jan. 1, 2023

Hybrid brain computer interfaces (BCI) utilizing the high temporal resolution of electroencephalography (EEG) and spatial near-infrared spectroscopy (fNIRS) are preferred over single-modal BCIs. However, due to large dimensionality multi-class statistical features commonly used in fNIRS signals, it is easy cause overfitting EEG-fNIRS hybrid BCI classifier. Therefore, a low-dimensional feature extraction method for based on EEG-informed general linear model (GLM) analysis proposed this paper. First, regression coefficient matrix obtained by using GLM with time window added, common pattern (CSP) extracted as features. Lastly, were combined CSP from optimal narrow band EEG features, support vector machine (SVM) classify samples The was tested publicly available motor imagery dataset. classification accuracy signals alone reached 68.79% (oxygenated hemoglobin) 68.62% (deoxygenated hemoglobin), combining 79.48%, which higher than other existing methods same By method, problem poor performance solved, not only enriches processing but also improves tasks.

Language: Английский

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