Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System

Borgheai, Seyyed Bahram; McLinden, John; Zisk, Alyssa Hillary; Hosni, Sarah Ismail; Deligani, Roohollah Jafari; Abtahi, Mohammadreza; Mankodiya, Kunal; Shahriari, Yalda

doi:10.1109/tnsre.2020.2980772

Cited by 48 publications

(39 citation statements)

References 40 publications

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“…The comparison of the proposed system with existing EEG studies in Table 3 is mentioned to indicate that an fNIRS-based exoskeleton can be a potential research area for BCI and BMI applications. Borgheai et al (2020) proposed an fNIRS-based BCI system that shows communication and control for late-stage ALS patients (lose voluntary muscle control) with a maximum average accuracy of 81.3 ± 5.7%. The field of BMI is emerging as an assistive methodology and aiding patients with different disabilities (Min et al, 2010;Teo and Chew, 2014;Shik et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

et al. 2021

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Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain–machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier—support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.

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Section: Discussionmentioning

confidence: 99%

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

et al. 2021

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“…The VM paradigm incorporates mental calculation alongside commonly used visual stimulation in brain–computer interface (BCI) communication systems. This augmentation of mental arithmetic has been reported to compensate for ALS patient’s incompetence in performing visual tasks, particularly in the later stages of the disease ( Borgheai et al, 2020 ). Subjects were exposed to visual stimuli through a 23″ LCD monitor.…”

Section: Methodsmentioning

confidence: 99%

“…All subjects first participated in a familiarization session to be trained on the experimental protocol and then participated in a main experimental session. In this study, we used a novel visuo-mental (VM) paradigm as our experimental protocol, following our previous works, which showed its efficacy to evoke hemodynamic responses in ALS patients as well as healthy participants (Borgheai et al, 2020(Borgheai et al, , 2019. The VM paradigm incorporates mental calculation alongside commonly used visual stimulation in brain-computer interface (BCI) communication systems.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…In each run, participants were instructed to perform mental calculations using the 2 × 2 matrix of numbers intensified over the target character. The calculation included a simple addition/subtraction either diagonally (at the first flash) or vertically (at the second flash) within the intensified matrix, picking the greater value and multiplying it by two, as explained in more detail in our previous works (Borgheai et al, 2020(Borgheai et al, , 2019. The stimulation time was set to 300 ms, and the inter-stimulation interval (ISI) was set to 6 s. In total, for two runs of the VM paradigm, there were 28 target characters (14 for each run), with one row/column flashes (single-trial) for each target character.…”

Section: Experimental Protocolmentioning

confidence: 99%

“…Therefore, more suitable portable neuroimaging methods are required to capture network disruptions associated with executive dysfunction in the ALS cohort. Functional near-infrared spectroscopy (fNIRS) has been recently introduced as a non-invasive portable neuroimaging system to mirror hemodynamic perturbations in a range of neurodegenerative diseases, including ALS ( Maidan et al, 2016 ; Kahya et al, 2019 ; Borgheai et al, 2020 ). Compared to EEG, fNIRS systems provide higher spatial resolution and lower sensitivity to artifacts.…”

Section: Introductionmentioning

confidence: 99%

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Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

et al. 2020

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Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS’ brain network and could hypothetically extend to applications in other neurodegenerative diseases.

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Editorial for “Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation to Disease Severity”

Shaikh

2021

Magnetic Resonance Imaging

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Enhancing Communication for People in Late-Stage ALS Using an fNIRS-Based BCI System

Cited by 48 publications

References 40 publications

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

Editorial for “Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation to Disease Severity”

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