Brain-computer interfaces based on near-infrared spectroscopy and electroencephalography registration in post-stroke rehabilitation: a comparative study DOI Creative Commons
О. А. Мокиенко, R. Kh. Lyukmanov, Pavel Bobrov

et al.

Neurology neuropsychiatry Psychosomatics, Journal Year: 2024, Volume and Issue: 16(5), P. 17 - 23

Published: Oct. 19, 2024

Motor imagery training under the control of a brain-computer interface (BCI) facilitates motor recovery after stroke. The efficacy BCI based on electroencephalography (EEG-BCI) has been confirmed by several meta-analyses, but more convenient and noise-resistant method near-infrared spectroscopy in circuit (NIRS-BCI) practically unexamined; comparisons two types have not performed. Objective: to compare accuracy clinical NIRS-BCI EEG-IMC post-stroke rehabilitation. Material methods . group consisted patients from an uncontrolled study (n=15; 9 men 6 women; age – 59.0 [49.0; 70.0] years; stroke duration 7.0 [2.0; 10.0] months; upper limb paresis 47.0 [35.0; 54.0] points Fugl-Meyer Assessment for function evaluation FM-UL). was formed main randomized controlled trial “iMove” (n=17; 13 4 53.0 10.0 [6.0; 13.0] 33.0 [12.0; 53.0] Patients participated comprehensive rehabilitation program supplemented BCI-guided movement (average sessions). Results. Median average rates achieved 46.4 [44.2; 60.4]% NIRS 40.0 [35.7; 45.1]% EEG (p=0.004). For group, median maximum 66.2 [56.4; 73.7]%, EEGBCI 50.6 [43.0; 62.3]% (p=0.006). proportion who clinically significant improvement according ARAT FM-UL were comparable both groups. showed greater compared EEG-BCI Action Research Arm Test (ARAT; increase 5.0 [4.0; 8.0] 1.0 [0.0; 3.0] points; p=0.008), scale (an [1.0; 4.0 5.0] points, respectively; p=0.455). Conclusion advantage ease use practice. Achieving higher provides additional opportunities game feedback scenarios patient motivation.

Language: Английский

Brain–Computer Interfaces with Intracortical Implants for Motor and Communication Functions Compensation: Review of Recent Developments DOI Open Access
О. А. Мокиенко

Sovremennye tehnologii v medicine, Journal Year: 2024, Volume and Issue: 16(1), P. 78 - 78

Published: Feb. 28, 2024

Brain-computer interfaces allow the exchange of data between brain and an external device, bypassing muscular system. Clinical studies invasive brain-computer interface technologies have been conducted for over 20 years. During this time, there has a continuous improvement approaches to neuronal signal processing in order improve quality control devices. Currently, with intracortical implants completely paralyzed patients robotic limbs self-service, use computer or tablet, type text, reproduce speech at optimal speed. Studies regularly provide new fundamental on functioning central nervous In recent years, breakthrough discoveries achievements annually made sphere. This review analyzes results clinical experiments implants, provides information stages technology development, its main achievements.

Language: Английский

Citations

2
A Multiple Session Dataset of NIRS Recordings From Stroke Patients Controlling Brain-Computer Interface DOI Creative Commons
М. Р. Исаев, О. А. Мокиенко, R. Kh. Lyukmanov

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 28, 2024

Abstract This paper presents an open dataset of over 50 hours near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total 237 motor imagery brain–computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback presented based on the real– time data classification results. We provide experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl–Meyer), online performance, simple analysis hemodynamic response. assume that this can be useful for evaluating effectiveness various near– signal processing techniques in with cerebrovascular accidents.

Language: Английский

Citations

1
Differential Hemodynamic Responses to Motor and Tactile Imagery: Insights from Multichannel fNIRS Mapping DOI
Andrei Miroshnikov, Lev Yakovlev, Nikolay Syrov

et al.

Brain Topography, Journal Year: 2024, Volume and Issue: 38(1)

Published: Oct. 4, 2024

Language: Английский

Citations

1
A multiple session dataset of NIRS recordings from stroke patients controlling brain–computer interface DOI Creative Commons
М. Р. Исаев, О. А. Мокиенко, R. Kh. Lyukmanov

et al.

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Oct. 25, 2024

This paper presents an open dataset of over 50 hours near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total 237 motor imagery brain-computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback presented based on the real-time data classification results. We provide experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl-Meyer), online performance, simple analysis hemodynamic response. assume that this can be useful for evaluating effectiveness various near-infrared signal processing techniques in with cerebrovascular accidents.

Language: Английский

Citations

1
Brain-computer interfaces based on near-infrared spectroscopy and electroencephalography registration in post-stroke rehabilitation: a comparative study DOI Creative Commons
О. А. Мокиенко, R. Kh. Lyukmanov, Pavel Bobrov

et al.

Neurology neuropsychiatry Psychosomatics, Journal Year: 2024, Volume and Issue: 16(5), P. 17 - 23

Published: Oct. 19, 2024

Motor imagery training under the control of a brain-computer interface (BCI) facilitates motor recovery after stroke. The efficacy BCI based on electroencephalography (EEG-BCI) has been confirmed by several meta-analyses, but more convenient and noise-resistant method near-infrared spectroscopy in circuit (NIRS-BCI) practically unexamined; comparisons two types have not performed. Objective: to compare accuracy clinical NIRS-BCI EEG-IMC post-stroke rehabilitation. Material methods . group consisted patients from an uncontrolled study (n=15; 9 men 6 women; age – 59.0 [49.0; 70.0] years; stroke duration 7.0 [2.0; 10.0] months; upper limb paresis 47.0 [35.0; 54.0] points Fugl-Meyer Assessment for function evaluation FM-UL). was formed main randomized controlled trial “iMove” (n=17; 13 4 53.0 10.0 [6.0; 13.0] 33.0 [12.0; 53.0] Patients participated comprehensive rehabilitation program supplemented BCI-guided movement (average sessions). Results. Median average rates achieved 46.4 [44.2; 60.4]% NIRS 40.0 [35.7; 45.1]% EEG (p=0.004). For group, median maximum 66.2 [56.4; 73.7]%, EEGBCI 50.6 [43.0; 62.3]% (p=0.006). proportion who clinically significant improvement according ARAT FM-UL were comparable both groups. showed greater compared EEG-BCI Action Research Arm Test (ARAT; increase 5.0 [4.0; 8.0] 1.0 [0.0; 3.0] points; p=0.008), scale (an [1.0; 4.0 5.0] points, respectively; p=0.455). Conclusion advantage ease use practice. Achieving higher provides additional opportunities game feedback scenarios patient motivation.

Language: Английский

Citations

0