Merging fNIRS-EEG Brain Monitoring and Body Motion Capture to Distinguish Parkinsons Disease

Abtahi, Mohammadreza; Borgheai, Seyyed Bahram; Jafari, Roohollah; Constant, Nicholas; Diouf, Rassoul; Shahriari, Yalda; Mankodiya, Kunal

doi:10.1109/tnsre.2020.2987888

Cited by 50 publications

(38 citation statements)

References 45 publications

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“…EEG + fNIRS significantly increases the classification accuracy and enhance the number of commands (Fazli et al, 2012;Kaiser et al, 2014;Khan et al, 2014;Yin et al, 2015;Hong and Khan, 2017;Li et al, 2017;Liu Y. et al, 2017;Abtahi et al, 2020;Cicalese et al, 2020). Table 7 is evidence of enhancement in classification accuracy of using hybrid EEG-fNIRS signals for MI and ME tasks.…”

Section: Chiarelli Et Al (2018)mentioning

confidence: 88%

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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Section: Chiarelli Et Al (2018)mentioning

confidence: 88%

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

Khan

Naseer

Yazidi

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

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“…We will take advantages of the benefits of virtual environments for patient rehabilitation as well as the benefits of using sensors. In this sense, we plan to track a person’s movement through the “3 Space Mocap” sensors (YEI Technology, Portsmouth, OH, USA) [ 55 , 56 ]. The conjunction of both technologies will allow the patient to have a good immersive and interactive experience within the virtual environments developed in this work.…”

Section: Discussionmentioning

confidence: 99%

Virtual Reality-Based Framework to Simulate Control Algorithms for Robotic Assistance and Rehabilitation Tasks through a Standing Wheelchair

Ortiz

Palacios-Navarro

Andaluz

et al. 2021

Sensors

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The implementation of control algorithms oriented to robotic assistance and rehabilitation tasks for people with motor disabilities has been of increasing interest in recent years. However, practical implementation cannot be carried out unless one has the real robotic system availability. To overcome this drawback, this article presents the development of an interactive virtual reality (VR)-based framework that allows one to simulate the execution of rehabilitation tasks and robotic assistance through a robotic standing wheelchair. The virtual environment developed considers the kinematic and dynamic model of the standing human–wheelchair system with a displaced center of mass, since it can be displaced for different reasons, e.g.,: bad posture, limb amputations, obesity, etc. The standing wheelchair autonomous control scheme has been implemented through the Full Simulation (FS) and Hardware in the Loop (HIL) techniques. Finally, the performance of the virtual control schemes has been shown by means of several experiments based on robotic assistance and rehabilitation for people with motor disabilities.

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“…The objective of SVM is to create a hyperplane that optimizes the margins between the classes [45]. The SVM with radial basis function (RBF) kernel has been shown to model both linear and more complex decision boundaries [26,46,47]. It was performed using the fitcsvm and predict functions in MATLAB.…”

Section: H Multimodal Data Classificationmentioning

confidence: 99%

Investigating Frontal Neurovascular Coupling in Response to Workplace Design-Related Stress

et al. 2020

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This research seeks to examine the impact of workstation types on the coupling of neural and vascular activities of the prefrontal cortex (PFC). The design of the workstations was found to impair the performance, physical and mental health of employees. However, the mechanism underlying cognitive activity involved during workstation design-related stress effects in the PFC has not been fully understood. We used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to simultaneously measure electrical activity and hemoglobin concentration changes in the PFC. The multimodal signal was collected from 23 healthy adult volunteers who completed the Montreal imaging stress task in ergonomic and non-ergonomic workstations. A supervised machine learning method based on temporally embedded canonical correlation analysis (tCCA) was utilized to obtain the association between neural activity and local changes in hemoglobin concentrations to enhance localization and accuracy. The results showed deactivation in alpha power rhythms and oxygenated hemoglobin, as well as declined activation pattern of the fused data in the right PFC at the non-ergonomic workstation. Additionally, all participants at the non-ergonomic workstation experienced a substantial rise in salivary alpha-amylase activity in comparison with the ergonomic workstation, indicating the existence of high-stress levels. The proposed tCCA approach obtains excellent results in discriminating workstation types achieving accuracies of 98.8% and a significant improvement of 8.0% (p < 0.0001) and 9.4% (p < 0.0001) over EEG-only and fNIRS-only, respectively. Our study suggests the use of functional neuroimaging in designing the workplace as it provides critical information on the causes of workplace-related stress. INDEX TERMS Electroencephalography (EEG), ergonomic, functional near-infrared spectroscopy (fNIRS), oxygenated hemoglobin, prefrontal cortex (PFC).

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Merging fNIRS-EEG Brain Monitoring and Body Motion Capture to Distinguish Parkinsons Disease

Cited by 50 publications

References 45 publications

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

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

Virtual Reality-Based Framework to Simulate Control Algorithms for Robotic Assistance and Rehabilitation Tasks through a Standing Wheelchair

Investigating Frontal Neurovascular Coupling in Response to Workplace Design-Related Stress

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