The model purpose, defined at the beginning of the procedure, influences the model selection, the data collection and the model calibration.
In this technical note, we describe data collection and analysis from a calibration experiment undertaken to support the Medical Research Council Autism Imaging Multicentre Study (MRC-AIMS), a large-sample cross-sectional study of adult males with autism spectrum condition (ASC) conducted at three centres in the United Kingdom.
The dry cap BCI system was thus ready for use after roughly 15 minutes: 2 for electrode preparation, 8 for calibration data collection and 5 minutes for the classifier algorithm to learn from the calibration data.
The examiner was calibrated prior to data collection and the inter-examiner agreement during calibration was measured using kappa statistic (κ = 0.86).
Calibration, data collection and post-processing was performed using Cortex analysis software.
A unified framework for calibration, data collection and system identification is developed, and utilized to investigate potential state dependencies.
This perspectives paper uses archived experimental data to consider aspects of data collection and experiment design for the calibration data to maximize the quality of the predicted Ys in the forward models; that is, we assume that well-estimated forward models are effective in the inverse problem.
The instrument's optical design, data collection and processing, laboratory and in-field calibration methods are reported and discussed.
The VIMS software includes two main programs: an application for calibration and data collection and an application to carry out the analysis.
On-orbit operations consisted of 4.3 months of experiment setup, 11.6 months of science data collection, and 1.4 months of post-science calibrations.
