Abstract
Abstract: Electroencephalography (EEG) is an essential method used across diverse fields, including neurological diagnosis, cognitive neuroscience, sleep research, and mental health studies. It enables the investigation of neurophysiological functions by recording the brain's electrical activity. A wide variety of EEG and mobile-EEG systems are available on the market. However, adherence to the standards set by the International Federation of Clinical Neurophysiology (IFCN) is essential for ensuring high-quality data collection in clinical environments. The DreamMachine, a mobile EEG device that fully meets these standards, offers 24-channel recordings at a 250 Hz sampling rate, Bluetooth Low Energy (BLE), and additional capabilities to capture electrooculography (EOG) and electrocardiography (ECG) signals. With its low cost, it presents an affordable solution for EEG recording. The software architecture of the open-source DreamMachine is detailed in this study. Focus is placed on data compression and communication between the device and its companion Android application. The details of the Android application's features, including gain settings, bits per channel, filters, bit-shifting, and safety factors are investigated. Subsequently, the system's performance is evaluated through a standard eyes open/closed experiment, comparing its results with a laboratory EEG system across a significant number of participants to assess the performance of the DreamMachine system.
Authors
Paria Samimisabet, Laura Krieger, Marc Vidal De Palol, Deniz Gün and Gordon Pipa
https://doi.org/10.1016/j.ohx.2025.e00689