Electroencephalography (EEG) measurements were conducted during three sessions: at visits 2, 3 and 4 (Figure 1 ). EEG data was acquired using a 64-channel Ag/AgCl EEG placed according to the extended international 10–20 system (Waveguard EEG cap, ANT Neuro, the Netherlands) see channel layout in Figure 1 and was recorded using ASA™ software (ANT Neuro, The Netherlands). The signals were amplified, low-pass filtered (digital FIR filter 1,350 Hz cut-off) and sampled at 5 kHz (TMSi-64 REFA, Twente Medical Systems International, the Netherlands). Impedance of the scalp electrodes was kept below 5 kOhm to reduce polarization effects.
Asa software
Manufactured by ANT Neuro
Sourced in Netherlands, United States
The ASA™ software is a comprehensive data analysis platform developed by ANT Neuro for the acquisition and processing of neuroscientific data. It provides a versatile and user-friendly interface for managing and analyzing a wide range of neurophysiological signals, including EEG, MEG, and other modalities.
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Lab products found in correlation
5 protocols using asa software
1
EEG Measurement Protocol for Neuroscience Research
2
Slow Wave Sleep Stage Analysis
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Sleep spindles and ripples are predominantly prevalent in the N2 stage of slow wave sleep (38 , 39 (link)) therefore only N2 stage periods were chosen. For each patient 30 min of iEEG with a simultaneous scalp EEG was selected. Only periods with at least 1 h distance to subclinical or clinical seizure were considered.
EEG data were converted into binary format and high-pass-filtered using the “ASA” software” (ANT Neuro, Enschede, Netherlands) via 2nd Butterworth filter with a cut-off-frequency of 0.5 Hz. After that, EEG files were transformed into the edf-format. The first 5 min of every EEG were screened by a neurophysiologist and contacts with continuous artifacts in intracranial or scalp EEG were rejected from further analysis.
EEG data were converted into binary format and high-pass-filtered using the “ASA” software” (ANT Neuro, Enschede, Netherlands) via 2nd Butterworth filter with a cut-off-frequency of 0.5 Hz. After that, EEG files were transformed into the edf-format. The first 5 min of every EEG were screened by a neurophysiologist and contacts with continuous artifacts in intracranial or scalp EEG were rejected from further analysis.
3
EEG Data Collection and Preprocessing
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EEG data were collected at 1 KHz from 64 electrodes that were distributed across the scalp according to the 10/20 system in an ANT WaveGuard EEG cap and digitized using the ASA Software (ANT Neuro, Hengelo, NL, United States). The ground electrode was placed posterior to electrode FPz and the signal from each channel was referenced to a whole-head average. Eye-blinks were recorded using two vertical electrooculogram electrodes. Individual records were inspected for excessive blinking and we found that this was not an issue with our data. These signals were amplified, and the onset of each stimulus trial was recorded in the EEG trace using a low-latency digital trigger. Data were exported to MATLAB 2018a for offline analysis using customized scripts.
4
EEG Data Preprocessing and Artifact Removal
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EEG data were collected using Asa™ software (ANT Neuro, Hengelo, the Netherlands) and pre‐processed using custom MATLAB code. Each recording first underwent a low‐pass anti‐aliasing finite impulse response (FIR) filter with a passband frequency of 100 Hz, a stopband frequency of 125 Hz, a pass‐band ripple of 0.001 dB, and a stop‐band attenuation of 80 dB. Signals were then down‐sampled to 250 Hz and further filtered with a high‐pass FIR filter (passband frequency of 1 Hz, stopband frequency of 0.8 Hz, pass‐band ripple of 0.01 dB, and a stop‐band attenuation of 80 dB) and another low‐pass FIR filter (passband frequency of 45 Hz, stopband frequency of 49 Hz, pass‐band ripple of 0.01 dB and a stop‐band attenuation of 60 dB). After filtering, the data were visually examined and any epochs with electrical, EMG or eye movement artefacts were rejected. The data were then further de‐noised using independent component analysis (ICA); resultant components were visually inspected and any components (maximum of four) indicating EOG and ECG contamination were removed.
5
EEG Data Preprocessing and Analysis
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Offline data treatment was performed by means of EEGLAB software99 (link), ASA software (ANT Neuro system, The Netherlands) and in-house MATLAB-based tools. Data were resampled to 512 Hz, low pass (200 Hz) and high-pass (0.1 Hz) filtered, channels with abnormally high amplitude were interpolated, data were re-referenced to REST100 (link), visually inspected and portions of data presenting abnormally high amplitude were manually rejected. Artefactual components from eye movement (blinks and horizontal movement) were identified and rejected using the ICALabel plugin of the EEGLAB toolbox.
ERPs were calculated by averaging epochs extracted from − 1000 to 4000 ms of the stimulus event (i.e. memory cue: the appearance of the cue word) in the T and the NT conditions without baseline correction101 (link)–104 (link). After artifact rejection, we obtained a total of 4158 and 4259 epochs for the T and NT conditions respectively.
The significance in the ERPs (and their topographies) between conditions at the population level was calculated in EEGlab by permutation statistics (p < 0.05), corrected for multiple comparisons (ERPs from 64 electrodes) by using the false discovery rate (FDR) method.
ERPs were calculated by averaging epochs extracted from − 1000 to 4000 ms of the stimulus event (i.e. memory cue: the appearance of the cue word) in the T and the NT conditions without baseline correction101 (link)–104 (link). After artifact rejection, we obtained a total of 4158 and 4259 epochs for the T and NT conditions respectively.
The significance in the ERPs (and their topographies) between conditions at the population level was calculated in EEGlab by permutation statistics (p < 0.05), corrected for multiple comparisons (ERPs from 64 electrodes) by using the false discovery rate (FDR) method.
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