Article

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.

Langue : ANGLAIS