Recordings were performed in a sound-attenuating chamber (Type 400, Industrial Acoustic Company, North Aurora, IL, USA) with the animal stabilized in a custom-made stereotaxic apparatus positioned on a vibration-isolated table. Animal’s temperature was kept at 37°C with a feedback-controlled heating pad. Acoustic stimuli were digitally generated using custom-written Matlab functions (version 7.5, The MathWorks Inc, Natick, MA, USA, RRID:SCR_001622 ). The stimuli were transferred to a D/A converter (RP2.1 real-time processor, 97.7 kHz sampling rate, Tucker-Davis Technologies, Alachua, FL, USA) and delivered through custom-made earphones (acoustic transducer: DT 770 pro, Beyer Dynamics) fitted with plastic tubes (length 35 mm, diameter 5 mm) which were positioned in the outer ear canal ~4 mm in front of the eardrum.
Matlab functions
Manufactured by MathWorks
Sourced in United States
MATLAB functions are a collection of pre-built mathematical and analytical tools provided by MathWorks for use in the MATLAB programming environment. These functions cover a wide range of domains, including signal processing, optimization, machine learning, and more. MATLAB functions allow users to leverage advanced computational capabilities without the need to develop complex algorithms from scratch.
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Lab products found in correlation
Rp2.1 real time processor, by Tucker-Davis Technologies (1 mentions)
Actichamp amplifier, by BrainVision (1 mentions)
Eeglab toolbox, by MathWorks (1 mentions)
Spss for windows version 23, by IBM (1 mentions)
Stata 17.0 mp parallel edition, by StataCorp (1 mentions)
Quadro k600, by NVIDIA (1 mentions)
5 protocols using matlab functions
1
Sound-Attenuating Chamber Recording Protocols
2
EEG Acquisition and Microstate Analysis
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An actiCHamp amplifier (Brain Vision LLC, NC, United States) was used to amplify and digitize the EEG data at a sampling frequency of 512 Hz. The EEG data were stored in a PC running Windows 7 (Microsoft Corporation, Washington, DC, United States). EEG activity was recorded from 64 positions with active Ag/AgCl scalp electrodes (actiCAP electrodes, Brain Vision LLC, NC, United States). The ground and reference electrodes were placed on AFz and on FCz, respectively (see Figure 1 ).
Electroencephalography acquisition was carried out by NeuroRT Studio software (Mensia Technologies SA, Paris, France). The EEG signal processing procedure was performed using MATLAB functions (MathWorks Inc., Natick MA, United States), specifically the EEGLab toolbox (Delorme and Makeig, 2004 (link)). EEG microstates were extracted and characterized by LORETA-KEY v20170220 software (the Key Institute for Brain-Mind Research, Zurich, Switzerland). Statistical analyses were performed by SPSS for Windows, version 23.0 (IBM Inc., Chicago, IL, United States).
Electroencephalography acquisition was carried out by NeuroRT Studio software (Mensia Technologies SA, Paris, France). The EEG signal processing procedure was performed using MATLAB functions (MathWorks Inc., Natick MA, United States), specifically the EEGLab toolbox (Delorme and Makeig, 2004 (link)). EEG microstates were extracted and characterized by LORETA-KEY v20170220 software (the Key Institute for Brain-Mind Research, Zurich, Switzerland). Statistical analyses were performed by SPSS for Windows, version 23.0 (IBM Inc., Chicago, IL, United States).
3
Comparative Segmentation Analysis of Retinal Imaging
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All statistical analyses were performed using Stata 17.0 MP-Parallel Edition (StataCorp) or custom-written Matlab functions (Mathworks Inc). Continuous data were summarized as means and standard deviations (s.d.) and categorical data as frequencies and percentages. Evaluability was compared with a two-sample test of proportions, and under- and over-segmentation proportions using a Fisher’s exact test due to the small sample size in some cells and the paired comparison of evaluable versus non-evaluable images for the Canon and Horus devices was performed using an exact McNemar test. For all quantitative metrics, Canon segmented images were compared with those of Horus from the same eye using a Bland & Altman analysis [23 (link)], and agreement was quantified as mean difference and limits of agreement (1.96 times the s.d. of the differences). A Bradley & Blackwood (BB) test [24 (link)] was used to test the joint hypothesis of equal means and equal variances in the Bland & Altman analysis and a test of the regression of differences on sums was also performed [25 (link)]. For all statistical tests, the null hypothesis was rejected if α < 0.05.
4
Measuring Visual Contrast Sensitivity Function
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The experiment was conducted in a dimly illuminated room. Stimuli were displayed on a 40.0 × 30.0 cm CRT monitor (Sony G520; 85 Hz, resolution of 1,600 × 1,200 pixels) with self-programmed Matlab functions (Mathworks Inc.) using the Psychophysics toolbox (Brainard, 1997 (link)). Stimuli were displayed using an NVIDIA Quadro K600 graphics system and viewed binocularly. To avoid local cues for vertical/horizontal and position, the screen was delimited by a 30.0 cm diameter circular window cut in a black cardboard (Tzvetanov, 2012 (link)). Luminance values were obtained with the help of the contrast box switcher (Li et al., 2003 (link)), that allowed to extend luminance range digitization above 10 bits, and thus provided the necessary minimum contrast step for CSF measure. Calibration was performed each day.
The eye-to-screen distance was maintained with a chin rest and fixed at 4 meters for CSF measure and the experiments for TI test with high-SF, and 2 m for TI test at low-SF. Experiments were initiated by subjects with a keyboard press. Subjects were requested to fixate on a small black square on the screen center and stimuli would be presented 200 ms after fixation point disappearance. Subjects responded by pressing corresponding keyboard keys.
The eye-to-screen distance was maintained with a chin rest and fixed at 4 meters for CSF measure and the experiments for TI test with high-SF, and 2 m for TI test at low-SF. Experiments were initiated by subjects with a keyboard press. Subjects were requested to fixate on a small black square on the screen center and stimuli would be presented 200 ms after fixation point disappearance. Subjects responded by pressing corresponding keyboard keys.
5
64-Channel EEG Recording and Analysis
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EEG data were recorded using a 64-channel BioSemi Active Two system in a continuous mode at a digitization rate of 512 Hz, and stored on disk for later analysis. Eye blinks and movements were monitored through electrodes placed on both temples (horizontal electrooculogram), and another one below the left eye (vertical electrooculogram). The offset of all electrodes was kept below 40 mV. Eye blinks and movements were monitored by electrodes placed on both temples (horizontal electrooculagram), and another one below the left eye (vertical electrooculogram). During data acquisition, the activity at all channels was referred to the system's internal loop (CMS/DRL sensors). EEG data were analyzed using EEGLAB 13.1.1b software (Delorme & Makeig, 2004) (link) and in-house developed Matlab functions (The Mathworks). Data were referenced offline to the average of the left and right mastoids and high-pass, before applying a high-pass filter with a half-amplitude cutoff value of .1 Hz. Individual ERP epochs were created for each
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