Examination of effectiveness of kinaesthetic haptic feedback for motor imagery-based brain-computer interface training

Sakamaki, Isao; Tavakoli, Mahdi; Wiebe, Sandra A.; Adams, Kim

doi:10.1080/2326263x.2022.2114225

Cited by 8 publications

(4 citation statements)

References 52 publications

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“…We suppose that somatosensory mental images are easier to perform and the learning of tactile imagery is more regulated and understandable compared to motor imagery, thus TI-based BCIs could be more useful and lower barriers to entry compared to MI-BCIs. There are a lot of studies showing the usefulness of tactile feedback and tactile stimulation during motor imagery training and MI-BCI control [69-71]. We believe that the positive effect of TS on the learning on kinesthetic imagination of movements may be associated with the use of mental tactile sensations (i.e.…”

Section: Discussionmentioning

confidence: 98%

Event-Related Desynchronization induced by Tactile Imagery: an EEG Study

Yakovlev

Syrov

Miroshnikov

et al. 2022

Preprint

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It is well known that both the movement of the hand itself and the mental representation of it lead to event-related desynchronization (ERD) of EEG recorded over the corresponding motor areas of the cerebral cortex. Similarly, in somatosensory cortical areas, ERD occurs upon tactile stimulation of the hand, but whether this effect is caused by mental representation of sensations from tactile stimulation remains poorly understood. In the present study, the effects on the EEG of imaginary vibrotactile sensations on the right hand were compared with the effects of real vibrotactile stimulation. Both actual vibrotactile stimulation and mental representation of it have been found to elicit contralateral ERD patterns, particularly prominent in the μ-band and most pronounced in the C3 region. The paper discusses tactile imagery as a part of the complex sensorimotor mental image and its prospects for using EEG patterns of imagery-induced tactile sensations as control signals in BCI circuits independently and when combined with ERD based on movement imagination to improve the efficiency of neurointerface technologies in rehabilitation medicine, in particular, to restore movements after a stroke and neurotrauma.

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Section: Discussionmentioning

confidence: 98%

Event-Related Desynchronization induced by Tactile Imagery: an EEG Study

Yakovlev

Syrov

Miroshnikov

et al. 2022

Preprint

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“…We suppose that somatosensory mental images are easier to perform, and the learning of tactile imagery is more regulated and understandable compared with motor imagery, thus TI-based BCIs could be more useful and lower barriers to entry compared with MI-BCIs. There are a lot of studies showing the usefulness of tactile feedback and tactile stimulation during motor imagery training and MI-BCI control (Zhang et al, 2021;Sakamaki et al, 2023;Zhong et al, 2023). We believe that the positive effect of TS on the learning on kinesthetic imagination of movements may be associated with the use of mental tactile sensations (i.e., participants can actually use the tactile imagery to increase the vividness of motor image and induce the sensorimotor rhythms depression more effectively).…”

Section: Tactile Imagery Perspectivesmentioning

confidence: 97%

Event-Related Desynchronization Induced by Tactile Imagery: an EEG Study

Yakovlev

Syrov

Miroshnikov

et al. 2023

eNeuro

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It is well-known that both hand movements and mental representations of movement lead to event-related desynchronization (ERD) of the electroencephalogram (EEG) recorded over the corresponding cortical motor areas. However, the relationship between ERD in somatosensory cortical areas and mental representations of tactile sensations is not well-understood. In this study, we employed EEG recordings in healthy humans to compare the effects of real and imagined vibrotactile stimulation of the right hand. Both real and imagined sensations produced contralateral ERD patterns, particularly in the μ-band and most significantly in the C3 region. Building on these results and the previous literature, we discuss the role of tactile imagery as part of the complex body image and the potential for using EEG patterns induced by tactile imagery as control signals in brain-computer interfaces (BCIs). Combining this approach with motor imagery could improve the performance of BCIs intended for rehabilitation of sensorimotor function after stroke and neural trauma.Significance StatementIn this study, we address the issue of mental representations in the somatosensory domain. By assessing the dynamics of sensorimotor EEG rhythms and the distribution of topographical EEG patterns, we demonstrate that tactile imagery produces event-related desynchronization in the contralateral EEG, even in the absence of physical stimulation. Our results clarify the neurophysiological mechanisms underlying the occurrence of ERD in the mu rhythm and its relationship to somatosensory cortical processing.

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“…[6][7][8][9]) and training users to produce more easily discernible signal patterns (e.g. [10][11][12][13][14][15][16][17][18][19]).…”

Section: Introductionmentioning

confidence: 99%

Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs

Ivanov,

Lio,

Chau

2023

J. Neural Eng.

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Objective. While electroencephalography (EEG)-based brain–computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user’s ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability. Here, we propose a combination of unsupervised clustering and Markov chain models to assess and describe user skill. Approach. Using unsupervised K-means clustering, we segmented the EEG signal space into regions representing pattern states that users could produce. A user’s movement through these pattern states while performing different tasks was modeled using Markov chains. Finally, using the steady-state distributions and entropy rates of the Markov chains, we proposed two metrics taskDistinct and relativeTaskInconsistency to assess, respectively, a user’s ability to (i) produce distinct task-specific patterns for each mental task and (ii) maintain consistent patterns during individual tasks. Main results. Analysis of data from 14 adolescents using a three-class BCI revealed significant correlations between the taskDistinct and relativeTaskInconsistency metrics and classification F1 score. Moreover, analysis of the pattern states and Markov chain models yielded descriptive information regarding user performance not immediately apparent from classification accuracy. Significance. Our proposed user assessment method can be used in concert with classifier-based analysis to further understand the extent to which users produce task-specific, time-evolving EEG patterns. In turn, this information could be used to enhance user training or classifier design.

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Examination of effectiveness of kinaesthetic haptic feedback for motor imagery-based brain-computer interface training

Cited by 8 publications

References 52 publications

Event-Related Desynchronization induced by Tactile Imagery: an EEG Study

Event-Related Desynchronization induced by Tactile Imagery: an EEG Study

Event-Related Desynchronization Induced by Tactile Imagery: an EEG Study

Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs

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