SignalFlowEEG Example Data

The SignalFlowEEG Example Data dataset contains sample EEG recordings that demonstrate the capabilities and usage of the SignalFlowEEG Python package. This package provides a comprehensive set of tools for processing, analyzing, and visualizing electroencephalography (EEG) data, with a focus on neuroscience research applications.The example dataset includes EEG recordings from various paradigms:Resting-state EEG: A 5-minute recording where the subject relaxed with eyes closed.Auditory chirp stimulation: EEG recorded while the subject listened to chirp sounds with varying frequencies.Visual evoked potentials: EEG recorded as the subject viewed checkerboard pattern stimuli to elicit visual responses.These recordings were collected at the Cincinnati Children's Hospital Medical Center and are made available for educational and testing purposes.SignalFlowEEG builds upon MNE-Python, a popular open-source library for EEG analysis, and offers additional functionality tailored for clinical research workflows. This example dataset allows users to explore SignalFlowEEG's features and gain hands-on experience analyzing EEG data with this powerful Python package.The dataset consists of .set files, a format used by the EEGLAB toolbox. Each file contains raw EEG data, channel info, and event markers for a specific experimental paradigm. Files can be loaded using mne.io.read_raw_eeglab() from MNE-Python, a SignalFlowEEG dependency. The dataset has no missing data or special abbreviations. Channel names and event markers follow standard EEGLAB conventions.