Society for Psychophysiological Research опубликовало в открытом доступе руководство для исследователей, работающих с размером зрачка:
Steinhauer, S. R., Bradley, M. M., Siegle, G. J., Roecklein, K. A. & Dix, A. (2022). Publication guidelines and recommendations for pupillary measurement in psychophysiological studies. Psychophysiology, 59, e14035. https://doi.org/10.1111/psyp.14035
Ссылка на него размещена на странице с другими руководствами от SPR (надо сказать, не очень новыми - большинству уже от 20 до 40 лет) https://sprweb.org/page/Guidelines_Papers
#guidelines #pupil
Steinhauer, S. R., Bradley, M. M., Siegle, G. J., Roecklein, K. A. & Dix, A. (2022). Publication guidelines and recommendations for pupillary measurement in psychophysiological studies. Psychophysiology, 59, e14035. https://doi.org/10.1111/psyp.14035
Ссылка на него размещена на странице с другими руководствами от SPR (надо сказать, не очень новыми - большинству уже от 20 до 40 лет) https://sprweb.org/page/Guidelines_Papers
#guidelines #pupil
Рекомендации по весьма актуальному сейчас подходу к анализу ЭЭГ и МЭГ - разделению осцилляций и апериодических 1/f компонентов:
Moritz Gerster, Gunnar Waterstraat, Vladimir Litvak, Klaus Lehnertz, Alfons Schnitzler, Esther Florin, Gabriel Curio, Vadim Nikulin. Separating neural oscillations from aperiodic 1/f activity: challenges and recommendations.
Neuroinformatics.
Accepted: 25 February 2022. Published online: 07 April 2022. https://doi.org/10.1007/s12021-022-09581-8
"In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them."
#methods #EEG #MEG #guidelines
Moritz Gerster, Gunnar Waterstraat, Vladimir Litvak, Klaus Lehnertz, Alfons Schnitzler, Esther Florin, Gabriel Curio, Vadim Nikulin. Separating neural oscillations from aperiodic 1/f activity: challenges and recommendations.
Neuroinformatics.
Accepted: 25 February 2022. Published online: 07 April 2022. https://doi.org/10.1007/s12021-022-09581-8
"In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them."
#methods #EEG #MEG #guidelines
SpringerLink
Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations
Neuroinformatics - Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law $$P\propto 1/{f}^{\beta }$$ and periodic components...
"dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods"
Andreas Keil, Edward M. Bernat, Michael X. Cohen, Mingzhou Ding, Monica Fabiani, Gabriele Gratton, Emily S. Kappenman, Eric Maris, Kyle E. Mathewson, Richard T. Ward, Nathan Weisz. Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series. Psychophysiology, Volume 59, Issue 5 e14052. First published: 10 April 2022. https://doi.org/10.1111/psyp.14052 [ Free Access ]
#methods #EEG #MEG #guidelines
Andreas Keil, Edward M. Bernat, Michael X. Cohen, Mingzhou Ding, Monica Fabiani, Gabriele Gratton, Emily S. Kappenman, Eric Maris, Kyle E. Mathewson, Richard T. Ward, Nathan Weisz. Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series. Psychophysiology, Volume 59, Issue 5 e14052. First published: 10 April 2022. https://doi.org/10.1111/psyp.14052 [ Free Access ]
#methods #EEG #MEG #guidelines