1. DECOMPOSITION OF SURFACE ELECTROMYOGRAMS: PRACTICAL EXPERIENCES A. Holobar 1,2 ales.holobar@delen.polito.it ( ales.holobar@uni-mb.si ) 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, 2007. 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:http://creativecommons.org/licenses/by-nc-sa/3.0/.http://creativecommons.org/licenses/by-nc-sa/3.0/

2. 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

3. Arrays of surface electrodes

4. 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

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

6. 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), 487-495. [pps]

7. 24681012 Time [s] Channel (4,3) 22.22.42.6 0 Time [s] Amplitude Reconstructed MUAP trains acquired EMG signal sum of reconstructed MUAP trains

8. Signal artefacts: line interference

9. 1234 5 13 12 11 10 9 8 7 6 5 4 3 2 1 Electrode rows Electrode columns Signal artefacts: bad contact (biceps brachii, monopolar mode)

10. Internal arrayCentral array External array 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Electrode rows Movement artefetcs & saturations: (external sphincter, bipolar mode, 100% MVC)

11. Decomposition & ground truth (external sphincter, bipolar mode)

12. Similar shapes of MUAPs: MU 1 23 Time 41 -250 0 250 -250 0 250 -250 0 250 -250 0 250 -250 0 250 -250 0 250 -250 0 250 -250 0 250 -250 0 250 -250 0 250 -250 0 250 -250 0 250 MUAPs amplitude [  V] 5

13. Similar shapes of MUAPs: MU 2 23 Time 41 -88 0 88 -88 0 88 -88 0 88 -88 0 88 -88 0 88 -88 0 88 -88 0 88 -88 0 88 -88 0 88 -88 0 88 -88 0 88 -88 0 88 MUAPs amplitude [  V] 5

14. Similar shapes of MUAPs: MU 1 & MU 2

15. Similar shapes of MUAPs and reconstruction of innervation pulse trains 23456789101112 MU 1 MU 2 MU 1 & 2 Time [s] Reconstructed innervation pulse trains

16. Case studies: ICA & image processing homepages ICA –http://www.tsi.enst.fr/icacentralhttp://www.tsi.enst.fr/icacentral Face recognition test databases –http://www.face-rec.org/databases/http://www.face-rec.org/databases/ –http://vision.bc.edu/~dmartin/MidLevel/http://vision.bc.edu/~dmartin/MidLevel/ Middlebury stereo page: –http://cat.middlebury.edu/stereo/http://cat.middlebury.edu/stereo/ –test database, source codes & algorithm benchmarking

17. ICA central: data collections

18. Face recognition test databases

20. Middlebury stereo page

23. 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.