Towards a mechanistic approach for the development of non‐invasive brain‐computer interfaces for motor rehabilitation

The Journal of Physiology, Journal Year: 2021, Volume and Issue: 599(9), P. 2361 - 2374

Published: March 17, 2021

Brain-computer interfaces (BCIs) designed for motor rehabilitation use brain signals associated with motor-processing states to guide neuroplastic changes in a state-dependent manner. These technologies are uniquely positioned induce targeted and functionally relevant plastic the human nervous system. However, while several studies have shown that BCI-based neuromodulation interventions may improve function patients lesions central system, neurophysiological structures processes BCI not been identified. In this review, we first summarize current knowledge of system learning new skills. Then, propose classification paradigms plasticity induction based on expected neural promoted. This proposes four two criteria: methods targeted. The existing evidence regarding circuits these different BCIs is discussed detail. proposed aims serve as starting point future trying elucidate underlying following interventions.

Language: Английский

---

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

Frontiers in Neurorobotics, Journal Year: 2022, Volume and Issue: 16

Published: Aug. 31, 2022

The constantly evolving human-machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, stress by monitoring brain states for optimum performance safety. Similarly, signals become paramount rehabilitation assistive purposes fields brain-computer interface (BCI) closed-loop neuromodulation neurological disorders motor disabilities. complexity, non-stationary nature, low signal-to-noise ratio of pose significant challenges researchers design robust reliable BCI accurately detect meaningful changes outside the laboratory environment. Different neuroimaging modalities are used hybrid settings enhance accuracy, increase control commands, decrease time required activity detection. Functional near-infrared spectroscopy (fNIRS) electroencephalography (EEG) measure hemodynamic electrical with a good spatial temporal resolution, respectively. However, settings, where both output BCI, their data compatibility due huge discrepancy between sampling rate number channels remains challenge real-time applications. Traditional methods, downsampling channel selection, result important information loss while making compatible. In this study, we present novel recurrence plot (RP)-based time-distributed convolutional neural network long short-term memory (CNN-LSTM) algorithm integrated classification fNIRS EEG acquired first projected into non-linear dimension RPs fed CNN extract features without performing any downsampling. Then, LSTM is learn chronological time-dependence relation activity. average accuracies achieved proposed model were 78.44% fNIRS, 86.24% EEG, 88.41% EEG-fNIRS BCI. Moreover, maximum 85.9, 88.1, 92.4%, results confirm viability RP-based deep-learning successful systems.

Language: Английский

---

Update on Stroke Rehabilitation in Motor Impairment

Brain & Neurorehabilitation, Journal Year: 2022, Volume and Issue: 15(2)

Published: Jan. 1, 2022

Motor impairment due to stroke limits patients' mobility, activities of daily living, and negatively affects their return the workplace. It also reduces quality life increases socioeconomic burden stroke. Therefore, optimizing recovery motor after is a very important goal for both individuals society as whole. The emergence improvement various technologies in Fourth Industrial Revolution have exerted major influence on development new rehabilitation methods efficiency enhancements existing methods. This review categorizes that promote function into upper limb lower summarizes recent advances rehabilitation. Although debate continues regarding effects some therapies, it hoped evidence will be improved through ongoing research so clinicians can treat patients with higher level evidence.

Language: Английский

---

Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials

Frontiers in Neuroscience, Journal Year: 2022, Volume and Issue: 16

Published: Aug. 3, 2022

Background Upper extremity dysfunction after stroke is an urgent clinical problem that greatly affects patients' daily life and reduces their quality of life. As emerging rehabilitation method, brain-machine interface (BMI)-based training can extract brain signals provide feedback to form a closed-loop rehabilitation, which currently being studied for functional restoration stroke. However, there no reliable medical evidence support the effect BMI-based on upper function This review aimed evaluate efficacy safety improving stroke, as well potential differences in different external devices. Methods English-language literature published before April 1, 2022, was searched five electronic databases using search terms including “brain-computer/machine interface”, “stroke” “upper extremity.” The identified articles were screened, data extracted, methodological included trials assessed. Meta-analysis performed RevMan 5.4.1 software. GRADE method used assess evidence. Results A total 17 studies with 410 post-stroke patients included. showed significantly improved motor [standardized mean difference (SMD) = 0.62; 95% confidence interval (CI) (0.34, 0.90); I 2 38%; p < 0.0001; n 385; random-effects model; moderate-quality evidence]. Subgroup meta-analysis indicated improves both chronic [SMD 0.68; CI (0.32, 1.03), 46%; 0.0002, model] subacute 1.11; 95%CI (0.22, 1.99); 76%; 0.01; compared control interventions, electrical stimulation (FES) (0.67, 1.54); 11%; 0.00001; model]or visual 0.66; (0.2, 1.12); 4%; 0.005; model;] devices BMI more effective than robot. In addition, activities living (ADL) interventions 1.12; (0.65, 1.60); 0%; 80; model]. There statistical dropout rate adverse effects between group group. Conclusion limb ADL patients. combined FES or may be better combination recovery trainings are well-tolerated associated mild effects.

Language: Английский

---

Effects of non-invasive brain stimulation on motor function after spinal cord injury: a systematic review and meta-analysis

Journal of NeuroEngineering and Rehabilitation, Journal Year: 2023, Volume and Issue: 20(1)

Published: Jan. 12, 2023

Abstract Background In recent years, non-invasive brain stimulation (NIBS) has been used for motor function recovery. However, the effects of NIBS in populations with spinal cord injury (SCI) remain unclear. This study aims to conduct a meta-analysis existing evidence on and safety against sham groups dysfunction after SCI provide reference clinical decision-making. Methods Two investigators systematically screened English articles from PubMed, MEDLINE, Embase, Cochrane Library prospective randomized controlled trials regarding recovery SCI. Studies at least three sessions were included. We assessed methodological quality selected studies using evidence-based Collaboration’s tool. A was performed by pooling standardized mean difference (SMD) 95% confidence intervals (CI). Results total 14 control involving 225 participants Nine repetitive transcranial magnetic (rTMS) five direct current (tDCS). The showed that could improve lower extremity strength (SMD = 0.58, CI 0.02–1.14, P 0.004), balance 0.64, 0.05–1.24, 0.03), decrease spasticity − 1.20 0.03, 0.04). ability upper not statistically significant compared those (upper-extremity strength: 0.97; function: 0.56; spasticity: 0.12). functional mobility did reach statistical significance when (sham groups). Only one patient reported seizures occurred during stimulation, no other types serious adverse events reported. Conclusion appears positively affect extremities patients, despite marginal P-value high heterogeneity. Further high-quality are needed support or refute use optimize parameters practice.

Language: Английский

---