Neuromuscular electrical stimulation induced brain patterns to decode motor imagery

Vidaurre, Carmen; Pascual, Javier; Ramos-Murguialday, Ander; Lorenz, Romy; Blankertz, Benjamin; Birbaumer, Niels; Müller, Klaus Robert

doi:10.1016/j.clinph.2013.03.009

Cited by 27 publications

(33 citation statements)

References 38 publications

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“…This idea was also supported by a clinical study that showed that MI can be well detected by including calibration data from passive movements [28]. Moreover, brain activation patterns induced by neuromuscular electrical stimulation (NMES) can also be used in MI-based BCI training in specific users [29]. For example, we showed that induced sensation with kinesthesia illusion by tendon vibration generates EEG signals that can be used for calibration of MI based BCI [30].…”

Section: Introductionmentioning

confidence: 59%

Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation

Yao¹,

Sheng

Mrachacz-Kersting

et al. 2018

IEEE Trans. Biomed. Eng.

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Abstract-Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real sensation) and somatosensory attentional orientation (SAO) provides a way to decode covert somatic attention. Subjects were instructed to sense the tactile stimulation, which was applied to the left (SS-L) or the right (SS-R) wrist. The BCI system was calibrated with the sensation data and then applied for online SAO decoding. Results: Both SS and SAO showed oscillatory activation concentrated on the contralateral somatosensory hemisphere. Offline analysis showed that the proposed calibration method led to greater accuracy than the traditional calibration method based on SAO only. This is confirmed by online experiments, where the online accuracy on 15 subjects was 78.8±13.1%, with 12 subjects >70% and 4 subject >90%. Conclusion: By integrating the stimulus-induced oscillatory dynamics from sensory cortex, covert somatosensory attention can be reliably decoded by a BCI system calibrated with tactile sensation. Significance: Indeed, real tactile sensation is more consistent during calibration than SAO. This brain-computer interfacing approach may find application for stroke and completely locked-in patients with preserved somatic sensation.

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Section: Introductionmentioning

confidence: 59%

Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation

Yao¹,

Sheng

Mrachacz-Kersting

et al. 2018

IEEE Trans. Biomed. Eng.

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“…For example, proprioception concurrent to MI has been shown to increase the decoding capability of classification algorithms for BCI Ramos-Murguialday and Birbaumer (2015); Corbet et al 2018; Vidaurre et al (2013Vidaurre et al ( , 2019.…”

Section: Discussionmentioning

confidence: 99%

“…SMR are oscillatory signals generated in the sensorimotor areas of the cortex. In general, oscillatory signals are divided within frequency ranges, where µ (9-14 Hz) and β (15-25 Hz) bands play a specially important role in MI feature extraction (Neuper and Pfurtscheller, 2001;Wolpaw, 2007;Millán et al, 2010;Vidaurre et al, 2013;Blankertz et al, 2011;Sannelli et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance

Vidaurre

Haufe

Jorajuría

et al. 2020

Preprint

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Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. For BCIs based on the modulation of sensorimotor rhythms as measured by means of electroen-cephalography (EEG), about 20% of potential users do not obtain enough accuracy to gain reliable control of the system. This lack of efficiency of BCI systems to decode user’s intentions requires the identification of neuro-physiological factors determining ‘good’ and ‘poor’ BCI performers. Given that the neuronal oscillations, used in BCI, demonstrate rich a repertoire of spatial interactions, we hypothesized that neuronal activity in sensorimotor areas would define some aspects of BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in calibration and an online feedback session in the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post-as well as pre-stimulus connectivity in the calibration recordings is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study shows that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sen-sorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.

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“…However, decoding one subject’s brain activity using data transferred from another subject is challenging and requires additional regularization of the spatial filters [46]. Yet another plausible solution for training the classifier might be to use data collected during passive movements, as suggested by Kaiser and co-workers [47], or during functional electrical stimulation of the target muscles [48]. …”

Section: Discussionmentioning

confidence: 99%

Comparing Features for Classification of MEG Responses to Motor Imagery

Halme

Parkkonen

2016

PLoS ONE

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BackgroundMotor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest.MethodsMEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio—spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject.ResultsThe SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results.ConclusionsWe obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.

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Neuromuscular electrical stimulation induced brain patterns to decode motor imagery

Cited by 27 publications

References 38 publications

Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation

Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation

Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance

Comparing Features for Classification of MEG Responses to Motor Imagery

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