The EEG was recorded using an EASYCAP 64-channel system and a Brainvision actiCHamp amplifier. The electrodes were arranged in accordance with the standard 10–10 system. The data was recorded at a sampling rate of 1000 Hz and filtered online between 0.03 Hz and 100 Hz. All electrodes were referenced online to the Fz electrode. Offline preprocessing was performed in MATLAB, using the FieldTrip toolbox (Oostenveld et al., 2011 (link); RRID:SCR_004849 ). The continuous EEG data were epoched into trials ranging from 200 ms before stimulus onset to 800 ms after stimulus onset, and baseline corrected by subtracting the mean of the pre-stimulus interval for each trial and channel separately. Trials containing movement-related artefacts were automatically identified and removed using the default automatic rejection procedure implemented in Fieldtrip. Channels containing excessive noise were removed based on visual inspection. Blinks and eye movement artifacts were identified and removed using independent components analysis and visual inspection of the resulting components. The epoched data were down-sampled to 200 Hz.
64 channel system
Manufactured by BrainVision
The 64-channel system is a versatile and reliable lab equipment designed for various research applications. It features 64 high-quality recording channels, enabling comprehensive data collection and analysis. The system is engineered to provide precise and accurate measurements, making it a valuable tool for researchers in diverse fields.
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Lab products found in correlation
2 protocols using 64 channel system
1
EEG Preprocessing Protocol for Stimulus-Evoked Analysis
2
Preprocessing 64-Channel EEG Data Using EEGLAB
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To record EEG data, we used the EASYCAP 64-channel system with a Brainvision actiCHamp amplifier at a sampling rate of 1000 Hz and with an online filter between 0.03 and 100 Hz. The signal was online re-referenced to FCz. Electrode placement followed the standard 10–10 system. Data was preprocessed offline with the EEGLAB toolbox version 14 (Delorme and Makeig, 2004 (link)). This comprised a low-pass filter with a 50 Hz cut-off, trial epoching in a peri‑stimulus time window between −100 ms and 999 ms, and baseline-correction by subtracting the mean of the 100 ms prestimulus time window from the entire epoch. We used independent component analysis (ICA) to clean the data from ocular and muscular artefacts. To guide the visual inspection of components for removal we used SASICA (Chaumon et al., 2015 (link)). To identify horizontal eye movement components, we used external electrodes from the horizontal electrooculogram (HEOG). We detected blink artefact and vertical eye movements using the two frontal electrodes Fp1 and Fp2. On average, we removed 18 (SD=5) components per participant. We finally applied multivariate noise normalization on the preprocessed data to improve the signal-to-noise ratio and reliability of the data (Guggenmos et al., 2018 (link)).
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