Use of Imaginary Lower Limb Movements to Control Brain–Computer Interface Systems

Bobrova, E. V.; Reshetnikova, V. V.; Frolov, Alexander A.; Gerasimenko, Yu. P.

doi:10.1007/s11055-020-00940-z

Cited by 17 publications

(7 citation statements)

References 33 publications

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“…There are two main BCI strategies to improve the lives of individuals among stroke patients, i.e., assistive BCI and rehabilitative BCI ( Mane et al, 2020 ). In the last decade, rehabilitative BCI has emerged as one of the promising tools for lower-limb motor function restoration by adjusting neuronal plasticity in affected neural circuits ( Mane et al, 2020 ; Romero-Laiseca et al, 2020 ; Bobrova et al, 2021 ). In the field of BCI, a lower limb exoskeleton control system based on steady state visual evoked potentials (SSVEP) is an efficient BCI system, such as achieved accuracies of 91.3 ± 5.73% and an information transfer rate (ITR) of 32.9 ± 9.13 bits/min ( Kwak et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Enhanced lower-limb motor imagery by kinesthetic illusion

Wang

Shi

et al. 2023

Front. Neurosci.

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Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.

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Section: Introductionmentioning

confidence: 99%

Enhanced lower-limb motor imagery by kinesthetic illusion

Wang

Shi

et al. 2023

Front. Neurosci.

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“…In order for the NES from the EEG to be more consistent with the desired task, it is extremely important to use motor imagery (MI) for the desired action, which causes oscillations in sensorimotor rhythms in the motor regions of the brain [7]. In addition, according to Bobrova et al [8], it has been suggested that BCI systems based on FES s operate on the Hebbian learning principle, where the simultaneous excitation of the motor zones of the cortex during MI and the spinal cord structure stimulated by FES s leads to an improvement in the ability to control the movements of the paralyzed limb. With this association between the computer and the effector systems, better secondary functions are observed in intestinal, urinary, and sexual functions in addition to improvements in flexibility and control of fine motor skills using the limbs [2].…”

Section: Introductionmentioning

confidence: 99%

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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“…Among the possible strategies reported in the literature, the most successful noninvasive BCI paradigms are based on three main approaches: evoked response (P300), steady-state visually evoked potential (SSVEP), and motor imagery (MI) (Lee et al, 2019). However, research on electroencephalogram (EEG) based MI of lower limb movements toward BCI-controlled applications remains relatively scarce (Bobrova et al, 2020;Asanza et al, 2022). Many of these studies have only been tested in offline scenarios due to the complexity of the movements and experimental setups that produce unrealistic EEG signals when compared to experimental setups in online scenarios (Rodríguez-Ugarte et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface

Triana-Guzman

Orjuela-Cañón

Jutinico

et al. 2022

Front. Neuroinform.

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Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.

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Use of Imaginary Lower Limb Movements to Control Brain–Computer Interface Systems

Cited by 17 publications

References 33 publications

Enhanced lower-limb motor imagery by kinesthetic illusion

Enhanced lower-limb motor imagery by kinesthetic illusion

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

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface

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