DECOMPOSITION OF SURFACE ELECTROMYOGRAMS: PRACTICAL EXPERIENCES A. Holobar 1,2 ( ) 1 FEECS, University.

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Presentation transcript:

DECOMPOSITION OF SURFACE ELECTROMYOGRAMS: PRACTICAL EXPERIENCES A. Holobar 1,2 ( ) 1 FEECS, University of Maribor, Slovenia 2 LISiN, Politecnico di Torino, Italy Laboratorio di Ingegneria del Sistema Neuromuscolare e della Riabilitazione Motoria Politecnico di Torino, Italy Faculty of Electrical Engineering and Computer Science University of Maribor, Slovenia Copyright Ales Holobar, Some rights reserved. Content in this presentation is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. This license is more fully described at:

LISiN Politecnico di Torino Surface EMG acquisition systems (16, 64, 128 chs) HD electrode arrays stimulators EMG simulators information extraction techniques Signal & image processing TF & TS analysis HOS Cepstral analysis BSS/ICA MIMO, MISO identification SSL University of Maribor

Arrays of surface electrodes

Select time instant with high MU activity Step 2 Convolution Kernel Compensation (CKC) instantaneous discharge rate (Hz) time (s) Compensate MUAP shapes Step 1 Blindly reconstruct MU discharge pattern estimator Step 3 Filter out single MU discharge patterns Step 4 multichannel surface EMG

CKC decomposition: MU discharge patterns (abductor pollicis, force ramp contractions 0 % - 10 % MVC)

CKC decomposition: MU discharge patterns (Biceps Brachii, constant isometric contraction at 10 % MVC ) A. Holobar, D. Zazula. Correlation-based decomposition of surface EMG signals at low contraction forces, Medical & Biological Engineering & Computing, 2004, 42 (4), [pps]

Time [s] Channel (4,3) Time [s] Amplitude Reconstructed MUAP trains acquired EMG signal sum of reconstructed MUAP trains

Signal artefacts: line interference

Electrode rows Electrode columns Signal artefacts: bad contact (biceps brachii, monopolar mode)

Internal arrayCentral array External array Electrode rows Movement artefetcs & saturations: (external sphincter, bipolar mode, 100% MVC)

Decomposition & ground truth (external sphincter, bipolar mode)

Similar shapes of MUAPs: MU 1 23 Time MUAPs amplitude [  V] 5

Similar shapes of MUAPs: MU 2 23 Time MUAPs amplitude [  V] 5

Similar shapes of MUAPs: MU 1 & MU 2

Similar shapes of MUAPs and reconstruction of innervation pulse trains MU 1 MU 2 MU 1 & 2 Time [s] Reconstructed innervation pulse trains

Case studies: ICA & image processing homepages ICA – Face recognition test databases – – Middlebury stereo page: – –test database, source codes & algorithm benchmarking

ICA central: data collections

Face recognition test databases

Middlebury stereo page

Acknowledgement This research was supported by a Marie Curie Intra-European Fellowships within the 6th European Community Framework Programme, by CyberManS EU project, Slovenian Ministry of Higher Education, Science and Technology, Italian Ministry of Foreign Affairs, Slovenian Research Agency and Lagrange project.