Quasi-movements and attempted movements: a possible alternative to motor imagery in BCI-based neurorehabilitation
- Authors: Shishkin S.L.1, Berdyshev D.A.1,2, Yashin A.S.1,3, Zabolotniy A.Y.4, Ossadtchii A.E.1,4, Vasilyev A.N.1,3
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Affiliations:
- Moscow State University of Psychology and Education
- Moscow Institute of Physics and Technology
- Lomonosov Moscow State University
- National Research University “Higher School of Economics”
- Issue: Vol 18, No 4 (2023)
- Pages: 687-690
- Section: Conference proceedings
- URL: https://genescells.ru/2313-1829/article/view/623412
- DOI: https://doi.org/10.17816/gc623412
- ID: 623412
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Abstract
Motor imagery (MI) is a frequently used “mental trigger” for non-invasive brain-computer interfaces (BCI). Numerous studies have examined the effectiveness of MI-BCI for post-stroke rehabilitation. However, the results remain inconclusive. A potential obstacle to the effectiveness of this method could stem from an ongoing debate between the internal focus of mental activity (i.e., modeling of reality) inherent in MI and the perceived significance of sensory feedback from the actual physical environment in BCI-facilitated therapy. The requirement to allocate attentional resources to both internal actions and external consequences may contribute to the low accuracy of MI-BCI classifiers in most users. Moreover, internal focus of attention in MI may partially account for the consistent failures in combining MI-BCI with eye tracker-based interaction technologies, since external focus of attention is crucial for gaze control.
A potentially effective replacement for motor imagery in BCIs is attempted movements (AMs). Studies have shown that BCIs are more successful in decoding AMs than MI (e.g., [1]). AMs involve attempted, but unrealized movements caused by paralysis or amputation. Despite their potential, AMs have received little attention, possibly because of modeling challenges with healthy participants and the widespread popularity of MI-BCIs. One approach to modeling them in healthy subjects is to use quasi-movements (QM), which are voluntary movements that are minimized by the subject to such an extent that they eventually become undetectable by objective measures [2]. However, QM has been studied even less than AM since its discovery by V.V. Nikulin and colleagues [2]. This may be due to the inadequate understanding of the difference between QM and MI.
We recently proved that the sensorimotor rhythm’s event-related desynchronization in QM does not rely on the residual electromyogram (EMG), indicating that strict EMG control, which is often impossible, may not be necessary for QM contrary to prior views. As a result, QM can be embraced more frequently as an alternative to MI in BCIs [3]. Moreover, we substantiated and refined earlier findings [2] that QM possesses a striking similarity to actual movements [4]. Here, we present our initial findings on the asynchronous classification of QM. These findings may serve as a foundation for a real-time QM-BCI system.
We used the EEG data recorded from 23 participants who synchronized their QM and MI with rhythmic sound triplets. A convolutional neural network called SimpleNet [5], with high interpretability, was trained on a subset of individual data separately for QM and IM, compared to a referential non-motor task. The network was applied offline and was unaware of sound timing, to another subset in 1.5-second windows with 0.1-second steps. QM/IM were detected when four consecutive positive windows occurred with a refractory period of three seconds.
Due to the high variability in MI-BCI performance among untrained individuals, we only assessed classifier performance amongst participants exhibiting a TPR (true positive rate) greater than 0.5. We identified 7 such participants in QM and 5 in MI. QM showed better intention detection than MI, though not significantly, according to the Mann-Whitney test. The mean values ± standard deviation for TPR were 0.81±0.12 in QM and 0.77±0.12 in MI, while the false alarm rate (s–1) was 0.03±0.02 in QM and 0.04±0.03 in MI, and the response time (s) was 2.81±0.06 in QM and 2.86±0.10 in MI.
Our initial findings on asynchronous BCI modeling are consistent with previous studies that have demonstrated superior QM classification in synchronous paradigms as compared to MI [2]. Notably, only minor hyperparameter optimization was performed for SimpleNet, leaving ample room for improvement in classification. Given the superior classification of AM over MI [1] and the promising preliminary findings presented here, the use of AM by end-users appears to be a viable option. Utilizing QM in studies that model AM could likely lead to further promotion and development of this technique. Additionally, the comparable nature of QM, AM, and overt movement implies their applicability in conveying intention via gaze-controlled interfaces. Although overt motor confirmation is suitable, MI-BCIs have shown inadequacies in this regard.
Full Text
Motor imagery (MI) is a frequently used “mental trigger” for non-invasive brain-computer interfaces (BCI). Numerous studies have examined the effectiveness of MI-BCI for post-stroke rehabilitation. However, the results remain inconclusive. A potential obstacle to the effectiveness of this method could stem from an ongoing debate between the internal focus of mental activity (i.e., modeling of reality) inherent in MI and the perceived significance of sensory feedback from the actual physical environment in BCI-facilitated therapy. The requirement to allocate attentional resources to both internal actions and external consequences may contribute to the low accuracy of MI-BCI classifiers in most users. Moreover, internal focus of attention in MI may partially account for the consistent failures in combining MI-BCI with eye tracker-based interaction technologies, since external focus of attention is crucial for gaze control.
A potentially effective replacement for motor imagery in BCIs is attempted movements (AMs). Studies have shown that BCIs are more successful in decoding AMs than MI (e.g., [1]). AMs involve attempted, but unrealized movements caused by paralysis or amputation. Despite their potential, AMs have received little attention, possibly because of modeling challenges with healthy participants and the widespread popularity of MI-BCIs. One approach to modeling them in healthy subjects is to use quasi-movements (QM), which are voluntary movements that are minimized by the subject to such an extent that they eventually become undetectable by objective measures [2]. However, QM has been studied even less than AM since its discovery by V.V. Nikulin and colleagues [2]. This may be due to the inadequate understanding of the difference between QM and MI.
We recently proved that the sensorimotor rhythm’s event-related desynchronization in QM does not rely on the residual electromyogram (EMG), indicating that strict EMG control, which is often impossible, may not be necessary for QM contrary to prior views. As a result, QM can be embraced more frequently as an alternative to MI in BCIs [3]. Moreover, we substantiated and refined earlier findings [2] that QM possesses a striking similarity to actual movements [4]. Here, we present our initial findings on the asynchronous classification of QM. These findings may serve as a foundation for a real-time QM-BCI system.
We used the EEG data recorded from 23 participants who synchronized their QM and MI with rhythmic sound triplets. A convolutional neural network called SimpleNet [5], with high interpretability, was trained on a subset of individual data separately for QM and IM, compared to a referential non-motor task. The network was applied offline and was unaware of sound timing, to another subset in 1.5-second windows with 0.1-second steps. QM/IM were detected when four consecutive positive windows occurred with a refractory period of three seconds.
Due to the high variability in MI-BCI performance among untrained individuals, we only assessed classifier performance amongst participants exhibiting a TPR (true positive rate) greater than 0.5. We identified 7 such participants in QM and 5 in MI. QM showed better intention detection than MI, though not significantly, according to the Mann-Whitney test. The mean values ± standard deviation for TPR were 0.81±0.12 in QM and 0.77±0.12 in MI, while the false alarm rate (s–1) was 0.03±0.02 in QM and 0.04±0.03 in MI, and the response time (s) was 2.81±0.06 in QM and 2.86±0.10 in MI.
Our initial findings on asynchronous BCI modeling are consistent with previous studies that have demonstrated superior QM classification in synchronous paradigms as compared to MI [2]. Notably, only minor hyperparameter optimization was performed for SimpleNet, leaving ample room for improvement in classification. Given the superior classification of AM over MI [1] and the promising preliminary findings presented here, the use of AM by end-users appears to be a viable option. Utilizing QM in studies that model AM could likely lead to further promotion and development of this technique. Additionally, the comparable nature of QM, AM, and overt movement implies their applicability in conveying intention via gaze-controlled interfaces. Although overt motor confirmation is suitable, MI-BCIs have shown inadequacies in this regard.
ADDITIONAL INFORMATION
Funding sources. The study was supported by the Russian Science Foundation, grant No. 22-19-00528.
About the authors
S. L. Shishkin
Moscow State University of Psychology and Education
Author for correspondence.
Email: sergshishkin@mail.ru
Russian Federation, Moscow
D. A. Berdyshev
Moscow State University of Psychology and Education; Moscow Institute of Physics and Technology
Email: sergshishkin@mail.ru
Russian Federation, Moscow; Moscow
A. S. Yashin
Moscow State University of Psychology and Education; Lomonosov Moscow State University
Email: sergshishkin@mail.ru
Russian Federation, Moscow; Moscow
A. Y. Zabolotniy
National Research University “Higher School of Economics”
Email: sergshishkin@mail.ru
Russian Federation, Moscow
A. E. Ossadtchii
Moscow State University of Psychology and Education; National Research University “Higher School of Economics”
Email: sergshishkin@mail.ru
Russian Federation, Moscow; Moscow
A. N. Vasilyev
Moscow State University of Psychology and Education; Lomonosov Moscow State University
Email: sergshishkin@mail.ru
Russian Federation, Moscow; Moscow
References
- Chen S, Shu X, Wang H, et al. The differences between motor attempt and motor imagery in brain-computer interface accuracy and event-related desynchronization of patients with with hemiplegia. Frontiers in Neurorobotics. 2021;15:706630. doi: 10.3389/fnbot.2021.706630
- Nikulin VV, Hohlefeld FU, Jacobs AM, Curio G. Quasi-movements: A novel motor-cognitive phenomenon. Neuropsychologia. 2008;46(2):727–742. doi: 10.1016/j.neuropsychologia.2007.10.008
- Vasilyev AN, Yashin AS, Shishkin SL. Quasi-movements and “quasi-quasi-movements”: Does residual muscle activation matter? Life. 2023;13(2):303. doi: 10.3390/life13020303
- Yashin AS, Shishkin SL, Vasilyev AN. Is there a continuum of agentive awareness across physical and mental actions? The case of quasi-movements (submitted). Consciousness and Cognition. 2023;112:103531. doi: 10.1016/j.concog.2023.103531
- Petrosyan A, Voskoboinikov A, Sukhinin D, et al. decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network. Journal of Neural Engineering. 2022;19(6). doi: 10.1088/1741-2552/aca1e1
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