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182 protocols using activetwo

1

Driving EEG with Eye Movement Monitoring

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While driving, the EEG (biosemi active system, Active two, BioSemi, NL) was recorded from 64 scalp electrode sites. EEG electrodes were arranged on the basis of the International 10–10 system and two additional electrodes were placed on the left and right mastoids. The Biosemi’s Active two amplifier uses a 2-wire active electrode system with a Common Mode Sensing and Driven Right Leg (CMS/DRL) principle. Data were sampled at 2048 Hz and a bandwidth of DC– 140 Hz. Additionally, six electro-oculography (EOG) electrodes were positioned around the two eyes to record horizontal and vertical eye movements. Electrode impedance was kept below 10 kΩ. The current position of the vehicle was continuously recorded by the EEG system.
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2

Multimodal Neurophysiological Data Collection

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In an electrically shielded room, EEG data was continuously recorded (DC–104 Hz; sampling rate (SR) 512 Hz) using Biosemi ActiveTwo amplifier, with 128 active electrodes mounted in a fitted flexible cloth cap according to the Biosemi 128 montage. Offline reference electrodes were attached at the mastoids. Electrooculography was measured with two bipolar electrode sets, mounted horizontally at the outer canthi of each eye, and vertically above and below the left eye.
Additionally, electrocardiography was measured from chest-mounted electrodes at the manubrium and lower left rib. Electrodermal activity was measured from the proximal phallanges of the index and forefinger on the non-dominant hand. These autonomic nervous system signals are not analysed herein.
Biosemi ActiveTwo equipment reduces the need for impedance measurement (http://www.biosemi.com/faq/cms&drl.htm). Instead, the quality of contact between electrode and skin was monitored using running average voltage offset at each electrode, which was kept below ±25 mV.
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3

High-Resolution EEG Voltage Recording

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The requirements for voltage recording are parallel data collection, low noise and the ability to save data for offline processing. EEG amplifiers offer an effective off the shelf solution with high performance systems such as the BioSemi ActiveTwo (Biosemi, Amsterdam, The Netherlands), actiCHamp (Brainproducts GmbH, Gilching, Germany) and g.tec HIamp (g.tec Medical Engineering GmbH, Graz, Austria) offering 24-bit resolution and a channel count up to 256. Each system offers a PC GUI for saving data to disk, data streaming over TCP/IP and the option to write custom software to interface with the device. These specifications come at the expense of maximum bandwidth, which in practice is limited by hardware anti-aliasing filters with typical cut off frequencies a tenth or a fifth of the sampling rate, Table 3. While this is sufficient for all brain EIT applications, Table 2, it may preclude use of the system in applications such as lung ventilation, which typically employ frequencies at 50 kHz or above [2 (link)]. For the experimental work presented here, either the BioSemi ActiveTwo (Biosemi, Amsterdam, the Netherlands) or actiCHamp (Brainproducts GmbH, Gilching, Germany) system was used for voltage recording.
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4

EEG Protocol for Offline Sleep Scoring

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EEG was recorded using a 32-channel system of Ag/AgCl active electrodes (Biosemi ActiveTwo, Amsterdam). In addition to the scalp electrodes, contacts were placed on the mastoids, next to the eyes, and on the chin. All recordings were made at 512 Hz. Sleep scoring based on the guidelines of the American Academy of Sleep Medicine25 was done online (while the participant was sleeping, for the purpose of controlling TMR cues) and, more formally, offline using EEGLAB26 (link) and sleepSMG (http://sleepsmg.sourceforge.net) packages for Matlab 2016b (MathWorks Inc., Natick, MA). For offline scoring, the electrophysiological data were re-referenced to the averaged mastoids and filtered using a two-way least-squares FIR bandpass filter between 0.4 Hz and 60 Hz (pop_eegfilt function in EEGLAB). Noisy channels were replaced with interpolated data from neighboring electrodes using the spherical interpolation method in EEGLAB. Offline scoring was done by two independent raters not informed about when sounds were presented.
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5

EEG Data Acquisition and Analysis

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The EEG data were recorded from seven electrodes (Cz, PO3, POz, PO4, O1, Oz, and O2) using a commercial biosignal recording system (ActiveTwo, BioSemi, Amsterdam, and the Netherlands). In addition, a pair of electrodes was attached above and below the right eye to acquire the vertical EOG data. The sampling rate was set at 2,048 Hz. The recorded EEG data were re-referenced to Cz [4 (link), 25 (link)] and then band-pass filtered at 6 and 50 Hz using a zero-phase Chebyshev type I infinite impulse response filter implemented in MATLAB (MathWorks, Inc., Natick, MA, USA). The program to analyze data in real time was developed using the FieldTrip toolbox [26 (link)].
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6

EEG Data Acquisition Protocol

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EEG data was obtained using 64 electrodes (Biosemi® ActiveTwo) arranged according to the international 10/20 extended system. Horizontal and vertical eye movements were monitored using four external electrodes. Horizontal EOG was recorded bipolarly from the outer canthi of both eyes and vertical EOG was recorded from above and below of the participant’s right eye. Two additional external electrodes were placed on the right and left mastoid to be used for later re-referencing.
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7

EEG Recording and Preprocessing Protocol

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The EEG data were obtained using 64 electrodes (Biosemi® ActiveTwo) arranged according to the international 10/20 extended system. Eye movements were monitored using four external electrodes. The horizontal EOG was recorded bipolarly from the outer canthi of both eyes, and the vertical EOG was recorded from above and below the participant’s right eye. Two electrodes were placed over the right and left mastoids for use as an offline reference. EEG, ECG, and EOG data were collected at a sampling frequency of 2,048 Hz. After the recordings, the data were downsampled to 1,024 Hz and re-referenced to mastoids using the MATLAB toolbox EEGLAB v7.1.7.18b (Delorme and Makeig, 2004 (link)).
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8

Continuous EEG during Rest and ATT Exposure

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Continuous EEG was recorded at rest before and after exposure to ATT. Each recording lasted approximately 6 min in duration, with 3 min eyes-open (EO), and 3 min eyes-closed (EC). The order of EO and EC was randomly assigned and then counterbalanced across participants. The experiment was conducted in a light- and sound-attenuated, electrical shielded room at ambient temperature. Participants were seated comfortably on a chair and were requested to minimize eye-blinks and physical movements during recording. Participants were monitored during recording to ensure they did not fall asleep. EEG data were recorded using a 64-electrode BioSemi ActiveTwo amplifier conforming to the international 10–20 system (Jasper, 1958 (link)). Electrodes were attached in standard formation (details of BioSemi referencing and grounding conventions2). The signal was digitized at 512 Hz with an open passband from 0.01 to 100 Hz. Horizontal and vertical electro-oculograms were recorded using separate electrodes placed above and below the right eye and at the outer canthi of both eyes.
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9

Heartrate Measurement Protocol

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HR was recorded from a Biosemi Active Two electroencephalography amplifier and three Ag/AgCl electrodes. An Ag/AgCl electrode was placed close to the heart and two Ag/AgCl electrodes (reference electrodes) were placed one inch apart on the neck. HR was measured as the peak-to-peak intervals of the heartbeat and calculated as the average number of heart beats per minute (Weintraub, et al., 2019) . Heartrate was averaged over 30-second epochs, resulting in four speech epochs and six discussion epochs (Owens & Beidel, 2015) .
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10

Bioelectrical Signal Processing and Analysis

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Bioelectrical signals were acquired at 512 Hz using a passive recording system (ActiveTwo, BioSemi, Netherlands). Signal processing and analysis was performed in the Gastrointestinal Electrical Mapping Suite (FlexiMap, Auckland, NZ)38 (link). Data were first down-sampled to 30 Hz before baseline drift was estimated and removed using a Gaussian moving median filter. A Savitzky-Golay filter (‘low-pass’, ~ 2 Hz) was then applied to reduce high-frequency noise39 (link). Slow-wave activation times (AT) were marked and clustered38 (link). Slow-wave propagation was visualised using isochronal AT maps showing the area of propagation per unit of time (Fig. 1)38 (link). Slow-wave amplitude, velocity, and frequency were calculated and mapped.
The occurrence of dysrhythmic activity was calculated as the duration of dysrhythmic activity (e.g., ectopic pacemakers, retrograde propagation, colliding wave fronts, conduction blocks, or electrical quiescence)4 (link),29 (link) divided by the total recorded duration. Each classification of normal versus dysrhythmic activity was subsequently verified by at least three other experienced investigators.
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