Denoising and Compression of Intracortical Signals with a Modified MDL Criterion

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2014 Mar 19

PMID 24638879

Citations 1

Authors

Elias S G Carotti

Vahid Shalchyan

Winnie Jensen

Dario Farina

Affiliations

Soon will be listed here.

Abstract

Intracortical signals are usually affected by high levels of noise [0 dB signal-to-noise ratio (SNR) is not uncommon] often due to magnetic or electrical coupling between surrounding sources and the recording system. Apart from hindering effective exploitation of the information content in the signals, noise also influences the bandwidth needed to transmit them, which is a problem especially when a large number of channels are to be recorded. In this paper, we propose a novel technique for joint denoising and compression of intracortical signals based on the minimum description length principle. This method was tested on both simulated and experimental signals, and the results showed that the proposed technique achieves improvements in SNR and compression ratios greater than alternative denoising/compression methods.

Citing Articles

Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface.

Wen S, Yin A, Tseng P, Itti L, Lebedev M, Nicolelis M Sci Rep. 2021; 11(1):19020.

PMID: 34561503 PMC: 8463672. DOI: 10.1038/s41598-021-98578-5.

References

Donoghue J . Bridging the brain to the world: a perspective on neural interface systems. Neuron. 2008; 60(3):511-21. DOI: 10.1016/j.neuron.2008.10.037. View

Quian Quiroga R, Nadasdy Z, Ben-Shaul Y . Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 2004; 16(8):1661-87. DOI: 10.1162/089976604774201631. View

Chen T, Ma T, Chen Y, Chen L . Low power and high accuracy spike sorting microprocessor with on-line interpolation and re-alignment in 90 nm CMOS process. Annu Int Conf IEEE Eng Med Biol Soc. 2013; 2012:4485-8. DOI: 10.1109/EMBC.2012.6346963. View

Craciun S, Cheney D, Gugel K, Sanchez J, Principe J . Wireless transmission of neural signals using entropy and mutual information compression. IEEE Trans Neural Syst Rehabil Eng. 2010; 19(1):35-44. DOI: 10.1109/TNSRE.2010.2070078. View

Yang Y, Mason A . Implantable neural spike detection using lifting-based stationary wavelet transform. Annu Int Conf IEEE Eng Med Biol Soc. 2012; 2011:7294-7. DOI: 10.1109/IEMBS.2011.6091701. View

Rizk M, Bossetti C, Jochum T, Callender S, Nicolelis M, Turner D . A fully implantable 96-channel neural data acquisition system. J Neural Eng. 2009; 6(2):026002. PMC: 2680289. DOI: 10.1088/1741-2560/6/2/026002. View

Shalchyan V, Jensen W, Farina D . Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings. IEEE Trans Biomed Eng. 2012; 59(9):2576-85. DOI: 10.1109/TBME.2012.2204991. View

Sodagar A, Wise K, Najafi K . A fully integrated mixed-signal neural processor for implantable multichannel cortical recording. IEEE Trans Biomed Eng. 2007; 54(6 Pt 1):1075-88. DOI: 10.1109/TBME.2007.894986. View

Kamboh A, Raetz M, Oweiss K, Mason A . Area-Power Efficient VLSI Implementation of Multichannel DWT for Data Compression in Implantable Neuroprosthetics. IEEE Trans Biomed Circuits Syst. 2013; 1(2):128-35. DOI: 10.1109/TBCAS.2007.907557. View

10.

Chae M, Yang Z, Yuce M, Hoang L, Liu W . A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter. IEEE Trans Neural Syst Rehabil Eng. 2009; 17(4):312-21. DOI: 10.1109/TNSRE.2009.2021607. View

11.

Hatsopoulos N, Donoghue J . The science of neural interface systems. Annu Rev Neurosci. 2009; 32:249-66. PMC: 2921719. DOI: 10.1146/annurev.neuro.051508.135241. View

12.

Nielsen M, Kamavuako E, Andersen M, Lucas M, Farina D . Optimal wavelets for biomedical signal compression. Med Biol Eng Comput. 2006; 44(7):561-8. DOI: 10.1007/s11517-006-0062-0. View

13.

Oweiss K . A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces. IEEE Trans Biomed Eng. 2006; 53(7):1364-77. DOI: 10.1109/TBME.2006.873749. View

14.

Cho J, Paiva A, Kim S, Sanchez J, Principe J . Self-organizing maps with dynamic learning for signal reconstruction. Neural Netw. 2007; 20(2):274-84. DOI: 10.1016/j.neunet.2006.12.002. View

15.

Perelman Y, Ginosar R . An integrated system for multichannel neuronal recording with spike/LFP separation, integrated A/D conversion and threshold detection. IEEE Trans Biomed Eng. 2007; 54(1):130-7. DOI: 10.1109/TBME.2006.883732. View

16.

Brechet L, Lucas M, Doncarli C, Farina D . Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection. IEEE Trans Biomed Eng. 2007; 54(12):2186-92. DOI: 10.1109/tbme.2007.896596. View

17.

Chang S, Yu B, Vetterli M . Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process. 2008; 9(9):1532-46. DOI: 10.1109/83.862633. View

18.

Kipke D, Shain W, Buzsaki G, Fetz E, Henderson J, Hetke J . Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities. J Neurosci. 2008; 28(46):11830-8. PMC: 3844837. DOI: 10.1523/JNEUROSCI.3879-08.2008. View