Brain-computer interfaces based on near-infrared spectroscopy and electroencephalography registration in post-stroke rehabilitation: a comparative study

Motor imagery training under the control of a brain-computer interface (BCI) facilitates motor recovery after stroke. The efficacy of BCI based on electroencephalography (EEG-BCI) has been confirmed by several meta-analyses, but a more convenient and noise-resistant method of near-infrared spectroscopy in the BCI circuit (NIRS-BCI) has been practically unexamined; comparisons of the two types of BCI have not been performed.

Objective: to compare the control accuracy and clinical efficacy of NIRS-BCI and EEG-IMC in post-stroke rehabilitation.

Material and methods. The NIRS-BCI group consisted of patients from an uncontrolled study (n=15; 9 men and 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 on the Fugl-Meyer Assessment for motor function evaluation of the upper limb – FM-UL). The EEG-IMC group was formed from the main group of the randomized controlled trial “iMove” (n=17; 13 men and 4 women; age – 53.0 [49.0; 70.0] years; stroke duration – 10.0 [6.0; 13.0] months; upper limb paresis – 33.0 [12.0; 53.0] points on the FM-UL). Patients participated in a comprehensive rehabilitation program supplemented by BCI-guided movement imagery training (average of 9 training sessions).

Results. Median of average BCI control rates achieved by the patients was 46.4 [44.2; 60.4]% in the NIRS group and 40.0 [35.7; 45.1]% in the EEG group (p=0.004). For the NIRS-BCI group, the median of the maximum BCI control accuracy achieved was 66.2 [56.4; 73.7]%, for EEGBCI – 50.6 [43.0; 62.3]% (p=0.006). The proportion of patients who achieved a clinically significant improvement according ARAT and the proportion of patients who achieved a clinically significant improvement according FM-UL were comparable in both groups. The NIRS-BCI group showed greater improvement in motor function compared to the EEG-BCI group according to Action Research Arm Test (ARAT; an increase of 5.0 [4.0; 8.0] points compared to an increase of 1.0 [0.0; 3.0] points; p=0.008), but not according to FM-UL scale (an increase of 5.0 [1.0; 10.0] and 4.0 [2.0; 5.0] points, respectively; p=0.455).

Conclusion. NIRS-BCI has an advantage in control accuracy and ease of use in clinical practice. Achieving higher control accuracy of BCI provides additional opportunities for the use of game feedback scenarios to increase patient motivation.

1. Mokienko OA, Lyukmanov RH, Bobrov PD, et al. Brain-computer interfaces for upper limb motor recovery after stroke: current status and development prospects (review). Sovremennyye tekhnologii v meditsine. 2023;15(6):63-74. doi: 10.17691/stm2023.15.6.07 (In Russ.).

2. Fedotova IR, Bobrov PD. Foundation and aspects of using motor imagery and brain computer interfaces in rehabilitation of children with cerebral palsy. Zhurnal vysshey nervnoy deyatel'nosti imeni I.P. Pavlova. 2022;72(1):8799. doi: 10.31857/S004446772201004X (In Russ.).

3. Carvalho R, Dias N, Cerqueira JJ. Brainmachine interface of upper limb recovery in stroke patients rehabilitation: A systematic review. Physiother Res Int. 2019 Apr;24(2):e1764. doi: 10.1002/pri.1764. Epub 2019 Jan 4.

4. Baniqued PDE, Stanyer EC, Awais M, et al. Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review. J Neuroeng Rehabil. 2021 Jan 23;18(1):15. doi: 10.1186/s12984-021-00820-8

5. Fu J, Chen S, Jia J. Sensorimotor RhythmBased Brain-Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review. Brain Sci. 2022 Dec 28;13(1):56. doi: 10.3390/brainsci13010056

6. Bai Z, Fong KNK, Zhang JJ, et al. Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis. J Neuroeng Rehabil. 2020 Apr 25;17(1):57. doi: 10.1186/s12984-020-00686-2

7. Kruse A, Suica Z, Taeymans J, Schuster-Amft C. Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke – a systematic review and meta-analysis. BMC Neurol. 2020 Oct 22;20(1):385. doi: 10.1186/s12883-020-01960-5

8. Yang W, Zhang X, Li Z, et al. The Effect of Brain-Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials. Front Neurosci. 2022 Feb 7;15:766879. doi: 10.3389/fnins.2021.766879

9. Mansour S, Ang KK, Nair KPS, et al. Efficacy of Brain-Computer Interface and the Impact of Its Design Characteristics on Poststroke Upper-limb Rehabilitation: A Systematic Review and Meta-analysis of Randomized Controlled Trials. Clin EEG Neurosci. 2022 Jan;53(1):79-90. doi: 10.1177/15500594211009065. Epub 2021 Apr 29.

10. Peng Y, Wang J, Liu Z, et al. The Application of Brain-Computer Interface in Upper Limb Dysfunction After Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Front Hum Neurosci. 2022 Mar 29;16:798883. doi: 10.3389/fnhum.2022.798883

11. Nojima I, Sugata H, Takeuchi H, Mima T. Brain-Computer Interface Training Based on Brain Activity Can Induce Motor Recovery in Patients With Stroke: A Meta-Analysis. Neurorehabil Neural Repair. 2022 Feb;36(2):8396. doi: 10.1177/15459683211062895. Epub 2021 Dec 27.

12. Xie YL, Yang YX, Jiang H, et al. Brain-machine interface-based training for improving upper extremity function after stroke: A meta-analysis of randomized controlled trials. Front Neurosci. 2022 Aug 3;16:949575. doi: 10.3389/fnins.2022.949575

13. Shou YZ, Wang XH, Yang GF. Verum versus Sham brain-computer interface on upper limb function recovery after stroke: A systematic review and meta-analysis of randomized controlled trials. Medicine (Baltimore). 2023 Jun 30;102(26):e34148. doi: 10.1097/MD.0000000000034148

14. Borisova VA, Isakova EV, Kotov SV. Possibilities of the brain-computer interface in the correction of post-stroke cognitive impairments. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova = S.S. Korsakov Journal of Neurology and Psychiatry. 2022;122(12-2):606. doi: 10.17116/jnevro202212212260 (In Russ.).

15. Kotov SV, Slyunkova EV, Borisova VA, Isakova EV. Effectiveness of brain-computer interfaces and cognitive training using computer technologies in restoring cognitive functions in patients after stroke. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova = S.S. Korsakov Journal of Neurology and Psychiatry. 2022;122(12-2):67-75. doi: 10.17116/jnevro202212212267 (In Russ.).

16. Soekadar SR, Kohl SH, Mihara M, von Lühmann A. Optical brain imaging and its application to neurofeedback. Neuroimage Clin. 2021;30:102577. doi: 10.1016/j.nicl.2021.102577. Epub 2021 Jan 26.

17. Huo C, Xu G, Xie H, et al. Functional near-infrared spectroscopy in non-invasive neuromodulation. Neural Regen Res. 2024 Jul 1;19(7):1517-22. doi: 10.4103/16735374.387970. Epub 2023 Nov 8.

18. Mihara M, Hattori N, Hatakenaka M, et al. Near-infrared spectroscopy-mediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims: a pilot study. Stroke. 2013 Apr;44(4):1091-8. doi: 10.1161/STROKEAHA.111.674507. Epub 2013 Feb 12.

19. Lyukmanov RKh, Isaev MR, Mokienko OA, et al. Brain-computer interface using functional near-infrared spectroscopy for post-stroke motor rehabilitation: Case Series. Annaly klinicheskoi i eksperimental'noi nevrologii = Annals of Clinical and Experimental Neurology. 2023;17(4):82-8. doi: 10.54101/ACEN.2023.4.10 (In Russ.).

20. Lee Friesen C, Lawrence M, Ingram TGJ, Boe SG. Home-based portable fNIRS-derived cortical laterality correlates with impairment and function in chronic stroke. Front Hum Neurosci. 2022 Dec 9;16:1023246. doi: 10.3389/fnhum.2022.1023246

21. Isaev MR, Mokienko OA, Lyukmanov RK, et al. A Multiple Session Dataset of NIRS Recordings From Stroke Patients Controlling Brain-Computer Interface. medRxiv. 2024. doi: 10.1101/2024.03.27.24304842

22. Frolov AA, Mokienko O, Lyukmanov R, et al. Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial. Front Neurosci. 2017 Jul 20;11:400. doi: 10.3389/fnins.2017.00400

23. Isaev MR, Bobrov PD. Effects of Selection of the Learning Set Formation Strategy and Filtration Method on the Effectiveness of a BCI Based on Near Infrared Spectrometry. Neurosci Behav Physiol. 2023;53(3):373-80. doi: 10.1007/s11055-023-01436-2

24. Mokienko OA, Suponeva NA. Diagnostics using motor scales. In the book: Stroke ьin adults: central paresis of the upper limb. Clinical guidelines. Moscow: MEDpress-Inform; 2018. P. 64 (In Russ.).

25. Mattke S, Cramer SC, Wang M, et al. Estimating minimal clinically important differences for two scales in patients with chronic traumatic brain injury. Curr Med Res Opin. 2020 Dec;36(12):1999-2007. doi: 10.1080/03007995.2020.1841616. Epub 2020 Nov 9.

26. Arya KN, Verma R, Garg RK. Estimating the minimal clinically important difference of an upper extremity recovery measure in subacute stroke patients. Top Stroke Rehabil. 2011 Oct;18 Suppl 1:599-610. doi: 10.1310/tsr18s01-599

27. Page SJ, Fulk GD, Boyne P. Clinically important differences for the upper-extremity Fugl-Meyer Scale in people with minimal to moderate impairment due to chronic stroke. Phys Ther. 2012 Jun;92(6):791-8. doi: 10.2522/ptj.20110009. Epub 2012 Jan 26.

28. Takeda K, Gomi Y, Kato H. Near-infrared spectroscopy and motor lateralization after stroke: a case series study. Int J Phys Med Rehabil. 2014;2(3):192-7. doi: 10.4172/23299096.1000192