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

Tombini, M and Rigosa, J and Zappasodi, F and Porcaro, C and Citi, L and Carpaneto, J and Rossini, PM and Micera, S (2012) 'Combined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control.' Neurorehabilitation and Neural Repair, 26 (3). pp. 275-281. ISSN 1545-9683

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

Item Type: Article
Uncontrolled Keywords: robotic hand prosthesis; LIFE; ERS/ERD analysis
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 11 Feb 2013 16:32
Last Modified: 15 Jan 2022 00:21

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