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Our tutorials aim at showing how to use the NBT GUI to visualize, pre-process, compute biomarkers and biomarker statistics for your data. We will mainly focus on how to analyze electroencephalography (EEG) signals.
For demonstration we use a demo EEG dataset. The demo dataset contains recordings of 129 EEG channels from 16 subjects in two conditions (eyes closed (ECR1) and eyes opened (EOR1)), downsampled at 200Hz. The format of the dataset follows the NBT standard (for more info see Importing data into nbt format).
Two versions of the data are available for download. The first version contains unprocessed (Raw) data, for the second (Clean) version artifacts have been removed with semi-automatic methods. You can use the Clean version to evaluate the quality of your processing of the Raw data.
Demoset: Raw NBT signals (1.6 Gb)
In the following tutorial we will show you how to process the Raw NBT signals. If you do not want to learn how to use NBT for artifact cleaning of EEG signals, but only want to try to compute biomarkers or biomarker statistics then download
Demoset: Clean NBT signals (1.0 Gb)
You can now start with the following tutorials:
Pre-processing
Artifact rejection: how to remove noisy channels and intervals from your signals
How to remove bad channels and transient artifacts
ICA - Independent Component Analysis
How to band-pass or high-pass filter a Signal in NBT
Basis to pass from temporal to frequency domain
The Discrete Fourier Transformation (DFT): Definition and numerical examples
Fourier transform and reconstruction of real signals
Analysis in frequency and/or temporal domain
Analysis of ongoing, evoked, and induced neuronal activity: Power spectra, wavelet analysis, and coherence
Semi-Automatic Data Cleaning
Automatic and Semi-Automatic methods for EEG pre-processing