title: 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.
authors: Campus Bio-Medico University, Rome, Italy. m.tombini@unicampus.it
authors: Mario
authors: M
authors: Tombini
authors: Jacopo
authors: J
authors: Rigosa
authors: Filippo
authors: F
authors: Zappasodi
authors: Camillo
authors: C
authors: Porcaro
authors: Luca
authors: L
authors: Citi
authors: Jacopo
authors: J
authors: Carpaneto
authors: Paolo Maria
authors: PM
authors: Rossini
authors: Silvestro
authors: S
authors: Micera
mesh_terms: D000328:Adult; D000672:Amputation Stumps / Q000503:physiopathology*; D000674:Amputees / Q000523:psychology / Q000534:rehabilitation*; D001931:Brain Mapping; D004569:Electroencephalography; D005500:Follow-Up Studies; D006225:Hand / Q000294:innervation*; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D008297:Male; D009044:Motor Cortex / Q000502:physiology*; D012371:Robotics; D013997:Time Factors