Combined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control

Background. Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. Objective. To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands. Methods. Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command. Results. Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (alpha/beta band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored alpha band synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months. Conclusions Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and mitigation of PLP.

Variance Based Measure for Optimization of Parametric Realignment Algorithms

Tomislav Milekovic

Neuronal responses to sensory stimuli or neuronal responses related to behaviour are often extracted by averaging neuronal activity over large number of experimental trials. Such trial-averaging is carried out to reduce noise and to diminish the influence of other signals unrelated to the corresponding stimulus or behaviour. However, if the recorded neuronal responses are jittered in time with respect to the corresponding stimulus or behaviour, averaging over trials may distort the estimation of the underlying neuronal response. Temporal jitter between single trial neural responses can be partially or completely removed using realignment algorithms. Here, we present a measure, named difference of time-averaged variance (dTAV), which can be used to evaluate the performance of a realignment algorithm without knowing the internal triggers of neural responses. Using simulated data, we show that using dTAV to optimize the parameter values for an established parametric realignment algorithm improved its efficacy and, therefore, reduced the jitter of neuronal responses. By removing the jitter more effectively and, therefore, enabling more accurate estimation of neuronal responses, dTAV can improve analysis and interpretation of the neural responses.

Public Library of Science2016

Self-grounded vision: hand ownership modulates visual location through cortical beta and gamma oscillations

Javier Bello Ruiz, Fosco Bernasconi, Olaf Blanke, Herberto Dhanis Pedro De Barros Camacho, Nathan Quentin Faivre, Roy Salomon, Michele Scandola

Vision is known to be shaped by context, defined by environmental and bodily signals. In the Taylor illusion, the size of an afterimage projected on one's hand changes according to proprioceptive signals conveying hand position. Here, we assessed whether the Taylor illusion does not just depend on the physical hand position, but also on bodily self-consciousness as quantified through illusory hand ownership. Relying on the somatic rubber hand illusion, we manipulated hand ownership, such that participants embodied a rubber hand placed next to their own hand. We found that an afterimage projected on the participant's hand drifted depending on illusory ownership between the participants' two hands, showing an implication of self-representation during the Taylor illusion. Oscillatory power analysis of electroencephalographic signals showed that illusory hand ownership was stronger in participants with stronger alpha suppression over left sensorimotor cortex, whereas the Taylor illusion correlated with higher beta/gamma power over frontotemporal regions. Higher gamma connectivity between left sensorimotor and inferior parietal cortex was also found during illusory hand ownership. These data show that afterimage drifts in the Taylor illusion do not only depend on the physical hand position but also on subjective ownership, which itself is based on the synchrony of somatosensory signals from the two hands. The effect of ownership on afterimage drifts is associated with beta/gamma power and gamma connectivity between frontoparietal regions and the visual cortex. Together, our results suggest that visual percepts are not only influenced by bodily context but are self-grounded, mapped on a self-referential frame.

2017