EMG and ENG-envelope pattern recognition for prosthetic hand control
| Autor: | Anna Lisa Ciancio, Rinaldo Sacchetti, Alberto Dellacasa Bellingegni, Angelo Davalli, Loredana Zollo, Emiliano Noce, Eugenio Guglielmelli |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Male Support Vector Machine Neuroprosthetics Computer science Movement Artificial Limbs Signal Synthetic data Pattern Recognition Automated 03 medical and health sciences 0302 clinical medicine Humans Muscle Skeletal Ulnar Nerve business.industry Electromyography General Neuroscience Pattern recognition Signal Processing Computer-Assisted Hand Median Nerve 030104 developmental biology Spike sorting Pattern recognition (psychology) Spike (software development) Artificial intelligence business 030217 neurology & neurosurgery Decoding methods Algorithms Envelope (motion) |
| Zdroj: | Journal of neuroscience methods. 311 |
| ISSN: | 1872-678X |
| Popis: | Background This paper proposes a new approach for neural control of hand prostheses, grounded on pattern recognition applied to the envelope of neural signals (eENG). New method The ENG envelope was computed by taking into account the amplitude and the occurrence of the spike in the neural recording. A pattern recognition algorithm applied on muscular signals was defined as a reference and a comparative analysis with traditionally adopted Spike Sorting Algorithms (SSA) for neural signals has been carried out. Method validation was divided in two parts: firstly, neural signals recorded from one amputee subject through intraneural electrodes were offline analyzed to discriminate between the two performed gestures; secondly, algorithm performance decay with the increase of the number of classes was studied through synthetic data. Results An accuracy of 98.26% with real data was reached with the pattern recognition applied to eENG. SSA reached an accuracy of 70%. Increasing the number of classes worsens the accuracy of this algorithm. Additionally, computational time for the pattern recognition applied to eENG is very low (32.6 μs for each sample in the data window analyzed). Comparison with existing method The eENG was proved to be more reliable in decoding the user intention than the SSA algorithm and it is computationally efficient. Conclusions It was demonstrated that it is possible to apply the well-known techniques of EMG pattern recognition to a conveniently processed neural signal and can pave the way to the application of neural gesture decoding in upper limb prosthetics. |
| Databáze: | OpenAIRE |
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