Dataset Information

Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study.

ABSTRACT: Hand function improvement in stroke survivors in the chronic stage usually plateaus by 6 months. Brain-computer interface (BCI)-guided robot-assisted training has been shown to be effective for facilitating upper-limb motor function recovery in chronic stroke. However, the underlying neuroplasticity change is not well understood. This study aimed to investigate the whole-brain neuroplasticity changes after 20-session BCI-guided robot hand training, and whether the changes could be maintained at the 6-month follow-up. Therefore, the clinical improvement and the neurological changes before, immediately after, and 6 months after training were explored in 14 chronic stroke subjects. The upper-limb motor function was assessed by Action Research Arm Test (ARAT) and Fugl-Meyer Assessment for Upper-Limb (FMA), and the neurological changes were assessed using resting-state functional magnetic resonance imaging. Repeated-measure ANOVAs indicated that long-term motor improvement was found by both FMA (F_[2,26] = 6.367, p = 0.006) and ARAT (F_[2,26] = 7.230, p = 0.003). Seed-based functional connectivity analysis exhibited that significantly modulated FC was observed between ipsilesional motor regions (primary motor cortex and supplementary motor area) and contralesional areas (supplementary motor area, premotor cortex, and superior parietal lobule), and the effects were sustained after 6 months. The fALFF analysis showed that local neuronal activities significantly increased in central, frontal and parietal regions, and the effects were also sustained after 6 months. Consistent results in FC and fALFF analyses demonstrated the increase of neural activities in sensorimotor and fronto-parietal regions, which were highly involved in the BCI-guided training. Clinical Trial Registration: This study has been registered at ClinicalTrials.gov with clinical trial registration number NCT02323061.

SUBMITTER: Lau CCY

PROVIDER: S-EPMC7855586 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study.

Lau Cathy C Y CCY Yuan Kai K Wong Patrick C M PCM Chu Winnie C W WCW Leung Thomas W TW Wong Wan-Wa WW Tong Raymond K Y RKY

Frontiers in human neuroscience 20210120

Hand function improvement in stroke survivors in the chronic stage usually plateaus by 6 months. Brain-computer interface (BCI)-guided robot-assisted training has been shown to be effective for facilitating upper-limb motor function recovery in chronic stroke. However, the underlying neuroplasticity change is not well understood. This study aimed to investigate the whole-brain neuroplasticity changes after 20-session BCI-guided robot hand training, and whether the changes could be maintained at ...[more]

PMID: 33551777

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Project description:Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot.

Dataset Information

Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study.

Publications

Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study.

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