Net amp 300 amplifier

Manufactured by Electrical Geodesics

The Net Amp 300 is an amplifier designed for use in electrophysiological research. It is capable of amplifying and digitizing low-level electrical signals generated by the brain or other biological systems. The device features multiple channels for simultaneous recording and provides adjustable gain and filtering options to optimize signal quality.

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Lab products found in correlation

Net station 4, by Electrical Geodesics (6 mentions) Geodesic sensor net, by Electrical Geodesics (3 mentions) Hydrocel net, by Electrical Geodesics (2 mentions) Hydrocel sensor net, by Electrical Geodesics (1 mentions) Matlab 8, by MathWorks (1 mentions)

7 protocols using net amp 300 amplifier

High-Density EEG Recording of Cognitive Tasks

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EEG signals were recorded using a 256 channel Geodesic Sensor Net (Electrical Geodesics Inc.; EGI, Eugene, OR, USA). All channels were referenced to the vertex (Cz) and collected with a high impedance amplifier, Net Amp 300 amplifier (Electrical Geodesics) using Net Station 4.5 software (Electrical Geodesics). Data were recorded continuously with a high-pass filter at 1 Hz and a sampling rate at 1000 Hz. Subjects were instructed to limit body movements, eye blinks, and muscular contractions during task selection and reward feedback.

Giustiniani J., Nicolier M., Teti Mayer J., Chabin T., Masse C., Galmès N., Pazart L., Trojak B., Bennabi D., Vandel P., Haffen E, & Gabriel D. (2020). Event-Related Potentials (ERP) Indices of Motivation during the Effort Expenditure for Reward Task. Brain Sciences, 10(5), 283.

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Investigating Shoulder Rotation via EEG

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The participants were fit with a 256-electrode EEG cap (HydroCel Sensor Net, Electrical Geodesics, Inc., Eugene, Oregon, USA). The cap was placed on the participant’s head after 10 practice trials of the AS task were completed to familiarize them with the experimental procedures. EEG data were sampled at 1000 Hz using a high-input impedance Net Amp 300 amplifier (Electrical Geodesics, Inc.) and NetStation 4.5.3 software. The EEG signals were referenced to the vertex electrode (Cz) during recording. The inputs from the splint apparatus were recorded by the EEG amplifier, using separate channels to record respective buttons for internal and external shoulder rotation movements. The onset of the stimulus was recorded by the EEG amplifier using a light detector (Cedrus, San Pedro, California, USA) for precise timing information synchronized with the EEG.

Lui K.K., Nunez M.D., Cassidy J.M., Vandekerckhove J., Cramer S.C, & Srinivasan R. (2020). Timing of readiness potentials reflect a decision-making process in the human brain. Computational brain & behavior, 4(3), 264-283.

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Resting-state EEG acquisition protocol

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Dense-array EEG was acquired at two time points. The first was at the Day 1 baseline visit and was used to address the primary study hypothesis. The second was at the Day 5 post-training visit and was used to examine a secondary hypothesis regarding change in EEG in relation to training-related behavioral gains. Awake, resting-state EEG was acquired for 180 s. Data were collected using a 256-lead Hydrocel net at 1,000 samples/s with a high input impedance Net Amp 300 amplifier and Net Station 4.5.3 software (ElectricalGeodesics, Inc., Eugene, OR). EEG signal was referenced to Cz during recording and re-referenced to the average of all leads for analysis. EEG signal was recorded without bandpass filters. During EEG acquisition, participants were instructed to hold still with the forearms resting on the anterior thigh and to direct their gaze at a fixation cross.

Zhou R.J., Hondori H.M., Khademi M., Cassidy J.M., Wu K.M., Yang D.Z., Kathuria N., Erani F.R., Dodakian L., McKenzie A., Lopes C.V., Scacchi W., Srinivasan R, & Cramer S.C. (2018). Predicting Gains With Visuospatial Training After Stroke Using an EEG Measure of Frontoparietal Circuit Function. Frontiers in Neurology, 9, 597.

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EEG Recordings During Decision-Making Tasks

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EEG signals were recorded using a 256 channel Geodesic Sensor Net (Electrical Geodesics Inc., EGI, Eugene, OR, United States) during both the IGT and EEfRT. All channels were referenced to the vertex (Cz) and collected with a high impedance amplifier (Net Amp 300 amplifier, Electrical Geodesics) using Net Station 4.5 software (Electrical Geodesics). Data were continuously recorded using a high-pass filter at 1 Hz with a sampling rate at 1000 Hz. For both the IGT and the EEfRT, subjects were instructed to limit body movements, eye blinks, and muscular contractions during task selection and reward feedback.

Giustiniani J., Nicolier M., Teti Mayer J., Chabin T., Masse C., Galmès N., Pazart L., Trojak B., Bennabi D., Vandel P., Haffen E, & Gabriel D. (2020). Behavioral and Neural Arguments of Motivational Influence on Decision Making During Uncertainty. Frontiers in Neuroscience, 14, 583.

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Resting-State and Task-Evoked EEG Acquisition

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Dense-array surface EEG was acquired using a 256-lead Hydrocel net (Electrical Geodesics, Inc., Eugene, OR). Awake resting-state EEG was acquired for three minutes. EEG signal was referenced to Cz during recording and re-referenced to the average of all leads for analysis; an advantage of this approach is that it minimizes common reference effects. A ground electrode was not used. EEG signal was recorded raw with no bandpass filter used.
During EEG-Rest, participants were asked to hold still with the forearms resting on the anterior thigh and to direct their gaze at a fixation cross displayed on the computer monitor. During EEG-Test 1, and subsequent recordings (EEG-Test 2 and EEG-Test 3), participants used their right hand to keep the cursor on the target, as above. Data were collected at 1000 Hz using a high input impedance Net Amp 300 amplifier (Electrical Geodesics) and Net Station 4.5.3 software (Electrical Geodesics).

Wu J., Srinivasan R., Kaur A, & Cramer S.C. (2014). Resting-state cortical connectivity predicts motor skill acquisition. NeuroImage, 91, 84-90.

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Preprocessing Dense-Array EEG Data

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Individuals completed at least one 3-minute awake, resting-state EEG recording using a dense-array 256 lead Hydrocel net (Electrical Geodesics, Eugene, OR) as previously described¹². EEG data were collected at a sampling rate of 1,000 Hz using a high input impedance Net Amp 300 amplifier and Net Station 4.5.3 software (Electrical Geodesics Inc.). Consistent with past work¹², raw and unfiltered EEG data were exported to MATLAB 8.5.0 (Mathworks, Natick, MA) for offline processing. Data were re-referenced to the mean signal across all leads. Of the 256 leads, 64 overlying cheek and neck regions were removed, leaving 192 leads for further analysis. A 50 Hz low-pass filter was applied with the remaining data segmented into 1-second non-overlapping epochs and mean detrended. Epochs consistent with muscle artifact were removed during visual inspection. Data underwent an Infomax independent components analysis using EEGLAB to remove ocular and cardiac artifacts^{30 (link)} and then transformed to electrode space for a second round of visual inspection.

Cassidy J.M., Wodeyar A., Wu J., Kaur K., Masuda A.K., Srinivasan R, & Cramer S.C. (2020). Low Frequency Oscillations Are A Biomarker Of Injury And Recovery After Stroke. Stroke, 51(5), 1442-1450.

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High-Density EEG Recording Protocol

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EEG signals were recorded using a 256 channel Geodesic Sensor Net (Electrical Geodesics Inc.; EGI, Eugene, OR). All channels were referenced to the vertex (Cz) and collected with a high impedance amplifier, a Net Amp 300 amplifier (Electrical Geodesics) using Net Station 4.5 software (Electrical Geodesics). Continuous recordings were performed with a high-pass set at 0.1 Hz and a sampling rate of 1000 Hz; all channels were referenced to the vertex (Cz) and impedances were below 50 kΩ. Subjects were instructed to limit body movements, eye blinks, and muscular contractions during the task activity and the reward feedback.

Teti Mayer J., Compagne C., Nicolier M., Grandperrin Y., Chabin T., Giustiniani J., Haffen E., Bennabi D, & Gabriel D. (2021). Towards a Functional Neuromarker of Impulsivity: Feedback-Related Brain Potential during Risky Decision-Making Associated with Self-Reported Impulsivity in a Non-Clinical Sample. Brain Sciences, 11(6), 671.

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