The experiments were programmed using the Psychophysics Toolbox (Brainard, 1997 (link)) and MATLAB 2012b (The MathWorks Inc., Natick, MA). Stimuli were presented on an LCD screen (22-in., 120 Hz, SAMSUNG 2233RZ) with optimal timing and luminance accuracy for vision researches (Wang & Nikolić, 2011 (link)). Resolution was set to 1,650 × 1,080 pixels, and the refresh rate to 60 Hz. Participants were seated in front of the monitor at a distance of 102 cm (eyes to fixation point). Responses were collected with a wireless mouse (Logitech M325). Accurate timing of stimuli (≤1 ms) was confirmed with a BioSemi Active-Two EEG amplifier system connected with a microphone and photodiode.
Active two eeg amplifier system
Manufactured by BioSemi
Sourced in Netherlands
The BioSemi Active Two EEG amplifier system is a device used for electroencephalography (EEG) data acquisition. It is designed to amplify and digitize signals from electrodes placed on the scalp to record the brain's electrical activity. The system features active electrodes and a high common-mode rejection ratio to provide high-quality EEG data.
Automatically generated - may contain errors
Lab products found in correlation
3 protocols using active two eeg amplifier system
1
Precise Vision Experiments with Optimized Setup
2
Resting-State EEG Acquisition and Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
During the EEG recording sessions, all the participants were required to sit comfortably in chair with their eyes opened and were instructed to remain still, avoiding active thought. All participants underwent a twice 5-min session of resting state EEG recording, separated by a 40-min interval. Continuous EEG data were recorded by a 64-channel Biosemi Active Two EEG amplifier system (Biosemi, Amsterdam, Netherlands) with Ag/AgCl scalp electrodes placed based on the international 10–20 system on an elastic cap. Before the EEG recording, the input impedance of all channels was kept below 5 kΩ to ensure the quality. Online EEG recording employed a bandpass filter ranging from 0.01 to 200 Hz, with a sampling rate of 2048 Hz.
The recorded EEG data was preprocessed by MATLAB R2016a (MathWorks, Natick, USA) and EEGLAB. Data were re-referenced to the whole brain and downsampled to 500 Hz. Offline EEG signal processing in EEGLAB included applying a bandpass filter from 1 to 30 Hz. All EEG data were visually inspected to identify and exclude segments with excessive noise, and noisy channels were interpolated using spherical spline interpolation. Additionally, eye movement artifacts (eye blinks and eye movements), muscle artifacts, and heart artifacts were corrected with independent component analysis impeded in EEGLAB.
The recorded EEG data was preprocessed by MATLAB R2016a (MathWorks, Natick, USA) and EEGLAB. Data were re-referenced to the whole brain and downsampled to 500 Hz. Offline EEG signal processing in EEGLAB included applying a bandpass filter from 1 to 30 Hz. All EEG data were visually inspected to identify and exclude segments with excessive noise, and noisy channels were interpolated using spherical spline interpolation. Additionally, eye movement artifacts (eye blinks and eye movements), muscle artifacts, and heart artifacts were corrected with independent component analysis impeded in EEGLAB.
3
EEG Processing for Artifact Removal
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Participants were seated in a sound-attenuated and electrostatically shielded room during the experiment. The continuous EEG data were recorded from a 64-channel Biosemi Active Two EEG amplifier system (Biosemi, Amsterdam, Netherlands) with Ag/AgCl scalp electrodes placed according to the international 10-20 system on an elastic cap.
During the online acquisition, EEG data were sampled at 2048 Hz with a bandpass filter of 0.01-200 Hz. The input impedance of all channels was kept below 5 kΩ.
The EEG data were processed offline by using custom-made scripts in MATLAB, the EEGLAB toolbox (Delorme and Makeig, 2004) , and sLORETA software (Pascual-Marqui, 2002) for source analyses. After down-sampling the data to 500 Hz, a built-in fourth-order Butterworth band-pass filter was applied with cutoff frequencies between 0.15 Hz and 40
Hz. Then, epochs lasting from 100 ms before the stimuli onset to 500 ms afterwards were extracted, among which those with unique, non-stereotypic artifacts were discarded.
Independent component analysis (ICA) was then performed, and components representing common ocular or cardiac artifacts were visually identified and removed for further analysis. Overall, less than 10% of all trials were rejected. Finally, data were re-referenced to the grand average of whole head.
During the online acquisition, EEG data were sampled at 2048 Hz with a bandpass filter of 0.01-200 Hz. The input impedance of all channels was kept below 5 kΩ.
The EEG data were processed offline by using custom-made scripts in MATLAB, the EEGLAB toolbox (Delorme and Makeig, 2004) , and sLORETA software (Pascual-Marqui, 2002) for source analyses. After down-sampling the data to 500 Hz, a built-in fourth-order Butterworth band-pass filter was applied with cutoff frequencies between 0.15 Hz and 40
Hz. Then, epochs lasting from 100 ms before the stimuli onset to 500 ms afterwards were extracted, among which those with unique, non-stereotypic artifacts were discarded.
Independent component analysis (ICA) was then performed, and components representing common ocular or cardiac artifacts were visually identified and removed for further analysis. Overall, less than 10% of all trials were rejected. Finally, data were re-referenced to the grand average of whole head.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!