EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies

Sarasola-Sanz, Andrea; Irastorza-Landa, Nerea; Shiman, Farid; López‐Larraz, Eduardo; Spüler, Martin; Birbaumer, Niels; Ramos-Murguialday, Ander

doi:10.1109/icorr.2015.7281204

Cited by 25 publications

(19 citation statements)

References 15 publications

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“…The importance of recalibration for rehabilitative systems has also been evaluated for other physiological signals, such as the electromyography [25]. Although there is still no evidence demonstrating that higher BMI accuracy provides better rehabilitative outcomes in BMItriggered therapies, it is reasonable to think that increasing the amount of times that the patient is positively rewarded may increase or accelerate the therapeutic effects.…”

Section: Discussionmentioning

confidence: 99%

Brain-machine interfaces for motor rehabilitation: Is recalibration important?

López‐Larraz

Trincado-Alonso

Montesano

2015

2015 IEEE International Conference on Rehabilitation Robotics (ICORR)

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Brain-machine interfaces (BMI) allow to decode motor commands from paralyzed patients' brains and use those commands with a rehabilitative or assistive purpose. However, brain non-stationarities can affect BMI performance over time in multi-session interventions. The amount and type of data used for calibration may play an important role on the posterior decoding performance. This paper studies six different schemes for BMI calibration, considering subjectspecific and subject-transfer scenarios. Data from a five-session rehabilitation intervention with four spinal cord injury patients is used to evaluate the decoding performance of the six proposed schemes. Our results show that recording some data at the beginning of each new session to recalibrate the BMI has a positive effect, although this effect is not achieved if we do not record enough number of trials. In addition, for subject-transfer approaches it is possible to achieve similar performances to those of subject-specific approaches for some subjects, but for others, generalization is not possible. These findings constitute a step forward towards the implantation of BMI for multiple-session rehabilitation therapies.

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Section: Discussionmentioning

confidence: 99%

Brain-machine interfaces for motor rehabilitation: Is recalibration important?

López‐Larraz

Trincado-Alonso

Montesano

2015

2015 IEEE International Conference on Rehabilitation Robotics (ICORR)

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“…The electromyography (EMG) has shown its feasibility for decoding arm, wrist and individual finger movements (Irastorza-Landa et al, 2017;Parker, Englehart, & Hudgins, 2006;Sarasola-Sanz et al, 2015;Tenore et al, 2009). It has been used for the control of prosthetic and robotic rehabilitative devices (Hesse et al, 2005;Zecca, Micera, Carrozza, & Dario, 2002;Zhou, Wang, Bao, Lü, & Wang, 2016).…”

Section: Cortico-muscular Hybrid Bmismentioning

confidence: 99%

“…A recent study proposed a biologically-inspired hybrid strategy, involving brain and muscle activity, to control a 7 degrees-of-freedom robotic arm, and demonstrated its viability in a moderately paralyzed stroke patient (Sarasola-Sanz et al, 2017). The hierarchical control used the EEG activity to monitor the intended movement of the subject (Sarasola-Sanz et al, 2016), and when the attempt of movement was detected, the EMG activity was sequentially used to estimate the kinematics of the robot (Sarasola-Sanz et al, 2015).…”

Section: Cortico-muscular Hybrid Bmismentioning

confidence: 99%

Brain-machine interfaces for rehabilitation in stroke: A review

López‐Larraz

Sarasola-Sanz

Irastorza-Landa

et al. 2018

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“…The exoskeleton rolls on top of a map with micro optical symbols printed on it, which are used to calculate the instantaneous position (more details can be found in [37,40]). The rest of the DoFs were recorded using motor encoders and potentiometers.…”

Section: Is-more Robotic Exoskeletonmentioning

confidence: 99%

“…flexion versus extension or pronation versus supination) were decoded with average accuracy ranging from 60 to 80% [36]. A few other groups have reported some preliminary work on multi-class decoding using motor imagery and execution of movements from the same upper limb [33,37,38]. Yong et al have shown a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp, and elbow movement [33].…”

Section: Introductionmentioning

confidence: 99%

Classification of different reaching movements from the same limb using EEG

et al. 2017

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Objective. Brain–computer-interfaces (BCIs) have been proposed not only as assistive technologies but also as rehabilitation tools for lost functions. However, due to the stochastic nature, poor spatial resolution and signal to noise ratio from electroencephalography (EEG), multidimensional decoding has been the main obstacle to implement non-invasive BCIs in real-live rehabilitation scenarios. This study explores the classification of several functional reaching movements from the same limb using EEG oscillations in order to create a more versatile BCI for rehabilitation. Approach. Nine healthy participants performed four 3D center-out reaching tasks in four different sessions while wearing a passive robotic exoskeleton at their right upper limb. Kinematics data were acquired from the robotic exoskeleton. Multiclass extensions of Filter Bank Common Spatial Patterns (FBCSP) and a linear discriminant analysis (LDA) classifier were used to classify the EEG activity into four forward reaching movements (from a starting position towards four target positions), a backward movement (from any of the targets to the starting position and rest). Recalibrating the classifier using data from previous or the same session was also investigated and compared. Main results. Average EEG decoding accuracy were significantly above chance with 67%, 62.75%, and 50.3% when decoding three, four and six tasks from the same limb, respectively. Furthermore, classification accuracy could be increased when using data from the beginning of each session as training data to recalibrate the classifier. Significance. Our results demonstrate that classification from several functional movements performed by the same limb is possible with acceptable accuracy using EEG oscillations, especially if data from the same session are used to recalibrate the classifier. Therefore, an ecologically valid decoding could be used to control assistive or rehabilitation mutli-degrees of freedom (DoF) robotic devices using EEG data. These results have important implications towards assistive and rehabilitative neuroprostheses control in paralyzed patients.

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EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies

Cited by 25 publications

References 15 publications

Brain-machine interfaces for motor rehabilitation: Is recalibration important?

Brain-machine interfaces for motor rehabilitation: Is recalibration important?

Brain-machine interfaces for rehabilitation in stroke: A review

Classification of different reaching movements from the same limb using EEG

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