What’s Your Next Move? Detecting Movement Intention for Stroke Rehabilitation

Zimmermann, Raphael; Marchal–Crespo, Laura; Lambercy, Olivier; Fluet, Marie-Christine; Metzger, Jean Claude; Edelmann, Janis; Brand, Johannes; Eng, Kynan; Riener, Robert; Wolf, M.; Gassert, Roger

doi:10.1007/978-3-642-36083-1_4

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…A relatively high classification accuracy of 85% was obtained over all subjects and sessions, which is in a similar range as other fNIRS studies investigating motor execution. 12 , 50 , 78 – 82 Three out of the 15 subjects (20% of the subjects) did not exceed the 70% significance threshold on a binary classification task. 52 When separating subjects into strong and weak responders, the former group achieved an average accuracy of 95%.…”

Section: Discussionmentioning

confidence: 94%

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

et al. 2022

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. Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications. Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information. Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility. Results: We found a high reproducibility of fNIRS measurements on a single-subject level ( and correlation ). SCR increased the reproducibility from 0.64 to 0.81 ( ) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations. Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.

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Section: Discussionmentioning

confidence: 94%

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

et al. 2022

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“… 64 Furthermore, the computational power required for the

approaches is higher, mainly due to the bandpass filtering and phase shifting of the regressor channels. We proposed the application of non-negative least squares also for the regression of other biosignals (e.g., blood pressure 10 , 65 and peripheral fNIRS measurements 38 ).…”

Section: Discussionmentioning

confidence: 99%

Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics

et al. 2020

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. Significance: The reliability of functional near-infrared spectroscopy (fNIRS) measurements is reduced by systemic physiology. Short-channel regression algorithms aim at removing systemic “noise” by subtracting the signal measured at a short source–detector separation (mainly scalp hemodynamics) from the one of a long separation (brain and scalp hemodynamics). In literature, incongruent approaches on the selection of the optimal regressor signal are reported based on different assumptions on scalp hemodynamics properties. Aim: We investigated the spatial and temporal distribution of scalp hemodynamics over the sensorimotor cortex and evaluated its influence on the effectiveness of short-channel regressions. Approach: We performed hand-grasping and resting-state experiments with five subjects, measuring with 16 optodes over sensorimotor areas, including eight 8-mm channels. We performed detailed correlation analyses of scalp hemodynamics and evaluated 180 hand-grasping and 270 simulated (overlaid on resting-state measurements) trials. Five short-channel regressor combinations were implemented with general linear models. Three were chosen according to literature, and two were proposed based on additional physiological assumptions [considering multiple short channels and their Mayer wave (MW) oscillations]. Results: We found heterogeneous hemodynamics in the scalp, coming on top of a global close-to-homogeneous behavior (correlation 0.69 to 0.92). The results further demonstrate that short-channel regression always improves brain activity estimates but that better results are obtained when heterogeneity is assumed. In particular, we highlight that short-channel regression is more effective when combining multiple scalp regressors and when MWs are additionally included. Conclusion: We shed light on the selection of optimal regressor signals for improving the removal of systemic physiological artifacts in fNIRS. We conclude that short-channel regression is most effective when assuming heterogeneous hemodynamics, in particular when combining spatial- and frequency-specific information. A better understanding of scalp hemodynamics and more effective short-channel regression will promote more accurate assessments of functional brain activity in clinical and research settings.

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“…Even though the use of such robot-assist platforms could significantly facilitate the rehabilitation process, nonetheless, the key to rehabilitation recovery is often dependent on the subjects' natural capability Zimmermann et al, 2015). In order to provide assistance and keep motivating the subjects in performing the prescribed tasks programmed on the robotic platform, the selection of a suitable controller is essential.…”

Section: Introductionmentioning

confidence: 99%

“…In order to provide assistance and keep motivating the subjects in performing the prescribed tasks programmed on the robotic platform, the selection of a suitable controller is essential. Several controllers have thus far been applied to the robot-platform to aid the patient in the movement recovery process Zimmermann et al, 2015;Proietti et al, 2016). It should be noted that in order to improve the interaction between the subject and the robotic-assisted platform, the capturing or detection of physiological motion intention data is non-trivial, as a substantial effort is required by the patient to complete the prescribed exercise.…”

Section: Introductionmentioning

confidence: 99%

The classification of movement intention through machine learning models: the identification of significant time-domain EMG features

Khairuddin

Sidek

Majeed

et al. 2021

PeerJ Computer Science

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Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject’s intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects’ biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.

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What’s Your Next Move? Detecting Movement Intention for Stroke Rehabilitation

Cited by 4 publications

References 29 publications

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

Characterizing reproducibility of cerebral hemodynamic responses when applying short-channel regression in functional near-infrared spectroscopy

Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics

The classification of movement intention through machine learning models: the identification of significant time-domain EMG features

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