Brain–computer interfaces for human gait restoration

Nenadic, Zoran

doi:10.1007/s11768-021-00070-y

Cited by 2 publications

(3 citation statements)

References 60 publications

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“…This is compatible with situations in which the participants in the present study presented a lower Ac CSP−LDA than usual, which contrasted with the more common situation where the activation of the interface occurred almost instantly. Similar findings were reported by Nenadic [24] and Liu [6].…”

Section: Success Percentage Of Fes S Activationsupporting

confidence: 91%

“…Performance gradually improved until reaching a more typical result for each of the participants. In Nenadic's study [24] it was found that participants take an average of four to five sessions to achieve their best performance. This suggests that performance may improve over time due to human-computer co-adaptation, and perhaps to other factors such as MI reactivation of dormant cortical areas.…”

Section: Success Percentage Of Fes S Activationmentioning

confidence: 99%

“…In this regard, problems in the batteries of the devices involved in the NES s -FES s interface were present more frequently. The efficiency of EEG itself has limitations in the implementation of the interface, as discussed by Nenadic [24]; although the possibility of highly accurate control in EEG-based BCIs has been demonstrated, the implementation of these systems outside the experimental environment may require very high levels of performance. This is due to possible reading errors, such as omissions or false positives, which were consistent in certain interventions.…”

Section: Study Limitationsmentioning

confidence: 99%

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The Learning Curve of People with Complete Spinal Cord Injury Using a NESs-FESs Interface in the Sitting Position: Pilot Study

Fiorin,

Sartori,

Méndez

et al. 2023

Eng

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The use of assistive technologies, such as a non-invasive interface for neuroelectrical signal and functional electrical stimulation (NESs-FESs), can mitigate the effects of spinal cord injury (SCI), including impairment of motor, sensory, and autonomic functions. However, it requires an adaptation process to enhance the user’s performance by tuning the learning curve to a point of extreme relevance. Therefore, in this pilot study, the learning curves of two people with complete SCI (PA: paraplegic-T6, and PB: quadriplegic-C4) were analyzed, with results obtained on the accuracy of the classifier (AcCSP−LDA), repetitions of intra-day training, and number of hits and misses in the activation of FESs for sixteen interventions using the NESs-FESs interface. We assumed that the data were non-parametric and performed the Spearman’s ρ test (and p-value) for correlations between the data. There was variation between the learning curves resulting from the training of the NESs-FESs interface for the two participants, and the variation was influenced by factors both related and unrelated to the individual users. Regardless of these factors, PA improved significantly in its learning curve, as it presented lower values in all variables in the first interventions compared to the PB, although only PA showed statistical correlation (on AcCSP−LDA values in RLL). It was concluded that despite the variations according to factors intrinsic to the user and the functioning of the equipment used, sixteen interventions were sufficient to achieve a good learning effect to control the NESs-FESs interface.

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Section: Success Percentage Of Fes S Activationsupporting

confidence: 91%

Section: Success Percentage Of Fes S Activationmentioning

confidence: 99%

Section: Study Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

The Learning Curve of People with Complete Spinal Cord Injury Using a NESs-FESs Interface in the Sitting Position: Pilot Study

Fiorin,

Sartori,

Méndez

et al. 2023

Eng

View full text Add to dashboard Cite

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Meta-Review on Brain-Computer Interface (BCI) in the Metaverse

Gholizadeh HamlAbadi,

Laamarti,

El Saddik

2024

ACM Trans. Multimedia Comput. Commun. Appl.

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This paper presents a comprehensive meta-review of the intersection between Brain-Computer Interface (BCI) technologies and the Metaverse, emphasizing the enhancement of immersive experiences through VR, AR, MR, XR, Digital Twin, and haptic interfaces. The study classifies BCI devices into wearable and non-wearable categories, with a focus on their applications in robotics. It explores BCI user feedback mechanisms and their impact on medical and non-medical settings, including personalized rehabilitation and immersive gaming. The review introduces two frameworks for leveraging the Metaverse to navigate multisensory integration between BCI and Assistive Devices. Applications such as VR therapies for stroke patients and neuro-responsive multiplayer gaming environments showcase the potential of BCIs to enhance Metaverse interactions. To the best of our knowledge, this is the first meta-review on the integration of BCI and the Metaverse, identifying key challenges and research gaps, and serves as a foundational reference for future research and development in this interdisciplinary field.

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Brain–computer interfaces for human gait restoration

Cited by 2 publications

References 60 publications

The Learning Curve of People with Complete Spinal Cord Injury Using a NESs-FESs Interface in the Sitting Position: Pilot Study

The Learning Curve of People with Complete Spinal Cord Injury Using a NESs-FESs Interface in the Sitting Position: Pilot Study

Meta-Review on Brain-Computer Interface (BCI) in the Metaverse

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