Quantification of signal-to-noise ratio in cerebral cortex recordings using flexible MEAs with co-localized platinum black, carbon nanotubes, and gold electrodes
| Autor: | Alex Suarez-Perez, Gemma Gabriel, Beatriz Rebollo, Xavi Illa, Anton Guimerà-Brunet, Javier Hernández-Ferrer, Maria Teresa Martínez, Rosa Villa, Maria V. Sanchez-Vives |
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| Přispěvatelé: | Ministerio de Ciencia, Innovación y Universidades (España), Generalitat de Catalunya, Suárez Pérez, Alex [0000-0002-3342-2889], Gabriel Buguna, Gemma [0000-0003-2140-6299], Illa, Xavi [0000-0002-3212-1128], Guimerà-Brunet, Anton [0000-0003-1768-3293], Hernández-Ferrer, Javier [0000-0002-6586-6935], Villa, Rosa [0000-0003-2735-3204], Sánchez-Vives, María V. [0000-0002-8437-9083], Suárez Pérez, Alex, Gabriel Buguna, Gemma, Illa, Xavi, Guimerà-Brunet, Anton, Hernández-Ferrer, Javier, Villa, Rosa, Sánchez-Vives, María V. |
| Rok vydání: | 2018 |
| Předmět: |
Materials science
Slow oscillations Acoustics 02 engineering and technology Carbon nanotube SNR Signal Noise (electronics) Radio spectrum lcsh:RC321-571 law.invention 03 medical and health sciences 0302 clinical medicine law lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Original Research Computer Science::Information Theory Quantitative Biology::Neurons and Cognition General Neuroscience Estimator Spectral density 021001 nanoscience & nanotechnology Signal-to-noise ratio (imaging) Electrode Neural recording Low impedance 0210 nano-technology Neural interfaces 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname Frontiers in Neuroscience Frontiers in Neuroscience, Vol 12 (2018) |
| Popis: | 6 Figuras Developing new standardized tools to characterize brain recording devices is critical to evaluate neural probes and for translation to clinical use. The signal-to-noise ratio (SNR) measurement is the gold standard for quantifying the performance of brain recording devices. Given the drawbacks with the SNR measure, our first objective was to devise a new method to calculate the SNR of neural signals to distinguish signal from noise. Our second objective was to apply this new SNR method to evaluate electrodes of three different materials (platinum black, Pt; carbon nanotubes, CNTs; and gold, Au) co-localized in tritrodes to record from the same cortical area using specifically designed multielectrode arrays. Hence, we devised an approach to calculate SNR at different frequencies based on the features of cortical slow oscillations (SO). Since SO consist in the alternation of silent periods (Down states) and active periods (Up states) of neuronal activity, we used these as noise and signal, respectively. The spectral SNR was computed as the power spectral density (PSD) of Up states (signal) divided by the PSD of Down states (noise). We found that Pt and CNTs electrodes have better recording performance than Au electrodes for the explored frequency range (5-1500 Hz). Together with two proposed SNR estimators for the lower and upper frequency limits, these results substantiate our SNR calculation at different frequency bands. Our results provide a new validated SNR measure that provides rich information of the performance of recording devices at different brain activity frequency bands ( This work was supported by Ministerio de Ciencia, Innovación y Universidades (Spain), BFU2017-85048-R and PCIN-2015-162-C02-01 (FLAG ERA) to MVSV, and by CERCA Programme/Generalitat de Catalunya. |
| Databáze: | OpenAIRE |
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