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Combined analysis of cortical (EEG) and nerve stump signals improves robotic hand control.

Abstract：

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 (α/β band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored α 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.

journal_name

Neurorehabil Neural Repair

journal_title

Neurorehabilitation and neural repair

authors

Tombini M,Rigosa J,Zappasodi F,Porcaro C,Citi L,Carpaneto J,Rossini PM,Micera S

doi

10.1177/1545968311408919

subject

Has Abstract

pub_date

2012-03-01 00:00:00

pages

275-81

issue

eissn

1545-9683

issn

1552-6844

pii

1545968311408919

journal_volume

pub_type

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Combined analysis of cortical (EEG) and nerve stump signals improves robotic hand control.