Reconstruction of flexor and extensor muscle activities from electroencephalography cortical currents

Yoshimura, Natsue; DaSalla, Charles S.; Hanakawa, Takashi; Sato, Masa‐aki; Koike, Yasuharu

doi:10.1016/j.neuroimage.2011.08.029

Cited by 43 publications

(37 citation statements)

References 36 publications

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“…Only one study, to our knowledge, has demonstrated that temporal activities of wrist muscles can be reconstructed from EEG cortical source currents, estimated from EEG sensor signals using a hierarchical Bayesian EEG inverse method [36]. Although their method could lead to drastic improvement in the non-invasive BMI area, it is still unknown whether it can be applied to sequential movements.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have reconstructed finger-movements, finger force, and arm EMG patterns from neural firings [32], blood oxygen level-dependent signals [33], near-infrared spectroscopy signals [34], cortical current dipoles [35], EEG signals [36], and local field potential (LFP) signals [37]. Since the SLiR algorithm can automatically select significant input variables and reduce the number of input dimensions, we used the Variational Bayesian Sparse Regression toolbox [38] to determine which band is effective in predicting EMG signals.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates

et al. 2012

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Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates

et al. 2012

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“…w 0 and w ij are, respectively, a bias term and a weight coefficient to the i -th filtered ECoG signal z i at time t - j Δ t (Figure 3B). We applied a Bayesian algorithm called sparse linear regression [44], [46]–[49] to determine values of the weights w ij .…”

Section: Methodsmentioning

confidence: 99%

Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex

et al. 2013

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Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson’s correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44∼0.73 and 0.18∼0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

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“…We believe this can be achieved based on the previous work done for example on motor-imagery two-dimensional cursor control (Wolpaw and McFarland, 2004). Other previous studies also discussed the possibilities of using EEG for such continuous control (Yoshimura et al, 2012). In addition, for the continuous two-dimensional feature control, explicit consideration of individual differences in cerebral recruitment during motor imagery may be necessary (Meulen et al, 2014).…”

Section: Discussion and Future Workmentioning

confidence: 99%

Brain-machine interfacing control of whole-body humanoid motion

Bouyarmane¹,

Vaillant²,

Sugimoto³

et al. 2014

Front. Syst. Neurosci.

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We propose to tackle in this paper the problem of controlling whole-body humanoid robot behavior through non-invasive brain-machine interfacing (BMI), motivated by the perspective of mapping human motor control strategies to human-like mechanical avatar. Our solution is based on the adequate reduction of the controllable dimensionality of a high-DOF humanoid motion in line with the state-of-the-art possibilities of non-invasive BMI technologies, leaving the complement subspace part of the motion to be planned and executed by an autonomous humanoid whole-body motion planning and control framework. The results are shown in full physics-based simulation of a 36-degree-of-freedom humanoid motion controlled by a user through EEG-extracted brain signals generated with motor imagery task.

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Reconstruction of flexor and extensor muscle activities from electroencephalography cortical currents

Cited by 43 publications

References 36 publications

Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates

Prediction of Muscle Activities from Electrocorticograms in Primary Motor Cortex of Primates

Prediction of Three-Dimensional Arm Trajectories Based on ECoG Signals Recorded from Human Sensorimotor Cortex

Brain-machine interfacing control of whole-body humanoid motion

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