000175927 001__ 175927
000175927 005__ 20181203022651.0
000175927 0247_ $$2doi$$a10.1177/1545968311408919
000175927 02470 $$2ISI$$a000300435300008
000175927 037__ $$aARTICLE
000175927 245__ $$aCombined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control
000175927 269__ $$a2012
000175927 260__ $$c2012
000175927 336__ $$aJournal Articles
000175927 520__ $$aBackground. 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.
000175927 6531_ $$arobotic hand prosthesis
000175927 6531_ $$aLife
000175927 6531_ $$aERS/ERD analysis
000175927 6531_ $$aPhantom Limb Pain
000175927 6531_ $$aIntraneural Electrodes
000175927 6531_ $$aAmputees
000175927 6531_ $$aMotor
000175927 6531_ $$aSynchronization
000175927 6531_ $$aClassification
000175927 6531_ $$aFeedback
000175927 700__ $$uUniv Campus Biomed, Neurol Clin, I-00128 Rome, Italy$$aTombini, Mario
000175927 700__ $$0247771$$g241251$$uScuola Super Sant Anna, BioRobot Inst, Pisa, Italy$$aRigosa, Jacopo
000175927 700__ $$uUniv G DAnnunzio, Chieti, Italy$$aZappasodi, Filippo
000175927 700__ $$uOsped Fatebenefratelli, ISTC CNR, Rome, Italy$$aPorcaro, Camillo
000175927 700__ $$uScuola Super Sant Anna, BioRobot Inst, Pisa, Italy$$aCiti, Luca
000175927 700__ $$uScuola Super Sant Anna, BioRobot Inst, Pisa, Italy$$aCarpaneto, Jacopo
000175927 700__ $$uIRCCS S Raffaele Pisana, Rome, Italy$$aRossini, Paolo Maria
000175927 700__ $$uScuola Super Sant Anna, BioRobot Inst, Pisa, Italy$$aMicera, Silvestro$$g218366$$0246201
000175927 773__ $$j26$$tNeurorehabilitation And Neural Repair$$q275-281
000175927 909C0 $$xU12522$$0252419$$pTNE
000175927 909C0 $$xU12599$$0252517$$pCNP
000175927 909CO $$pSTI$$particle$$ooai:infoscience.tind.io:175927
000175927 937__ $$aEPFL-ARTICLE-175927
000175927 973__ $$rREVIEWED$$sPUBLISHED$$aOTHER
000175927 980__ $$aARTICLE