An efficient stochastic based model for simulating microelectrode recordings of the deep brain

Weegink, K. J., Varghese, J. J., Bellette, P. A., Coyne, T., Silburn, P. A. and Meehan, P. A. (2012). An efficient stochastic based model for simulating microelectrode recordings of the deep brain. In: Proceedings of Biosignals 2012, International Conference on Bio-Inspired Systems and Signal Processing. 5th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC), Vilamoura, Portugal, (76-84). 1-4 February 2012.

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Author Weegink, K. J.
Varghese, J. J.
Bellette, P. A.
Coyne, T.
Silburn, P. A.
Meehan, P. A.
Title of paper An efficient stochastic based model for simulating microelectrode recordings of the deep brain
Language of Title eng
Conference name 5th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC)
Conference location Vilamoura, Portugal
Conference dates 1-4 February 2012
Proceedings title Proceedings of Biosignals 2012, International Conference on Bio-Inspired Systems and Signal Processing
Language of Proceedings Title eng
Language of Journal Name eng
Place of Publication Portugal
Publisher SciTePress
Publication Year 2012
Sub-type Fully published paper
Open Access Status
ISBN 9789898425898
Start page 76
End page 84
Total pages 9
Collection year 2013
Language eng
Abstract/Summary We have developed a computationally efficient stochastic model for simulating microelectrode recordings, including electronic noise and neuronal noise from the local field of 3000 neurons. From this we have shown that for a neuron network model spiking with a stationary Weibull distribution the power spectrum can change from exhibiting periodic behaviour to non-stationary behaviour as the distribution shape is changed. It is shown that the windowed power spectrum of the model follows an analytical result prediction in the range of 100-5000 Hz. The analysis of the simulation is compared to the analysis of real patient interoperative sub-thalamic nucleus microelectrode recordings. The model runs approximately 200 times faster compared to existing models that can reproduce power spectral behaviour. The results indicate that a spectrogram of the real patient recordings can exhibit non-stationary behaviour that can be re-created using this efficient model in real time.
Keyword Deep brain signals
Micro-electrode recordings
Point Process model
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Received award for "Best Student Paper"

 
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Created: Thu, 08 Mar 2012, 08:51:04 EST by Katie Gollschewski on behalf of School of Mechanical and Mining Engineering