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A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

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Enhancing mobile EEG: Software development and performance insights of the DreamMachine.

Paria Samimisabet¹, Laura Krieger¹, Marc Vidal De Palol¹

¹Institute of Cognitive Science, Osnabrueck University, 49074 Osnabrueck, Germany.

|September 22, 2025

Summary

The DreamMachine is a low-cost, mobile electroencephalography (EEG) device that meets clinical standards. Its open-source architecture and Android app facilitate accessible brain activity monitoring for research and mental health applications.

Keywords:

Android application DreamMachine EEGDroid Electroencephalography (EEG)Eyes open/closed Mobile EEG

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Area of Science:

Neuroscience
Biomedical Engineering
Medical Devices

Background:

Electroencephalography (EEG) is crucial for studying brain activity in clinical and research settings.
Existing EEG systems vary in quality and adherence to International Federation of Clinical Neurophysiology (IFCN) standards.
Mobile EEG offers potential for increased accessibility but requires rigorous validation.

Purpose of the Study:

To detail the software architecture of the open-source DreamMachine, a mobile EEG device.
To evaluate the performance and clinical applicability of the DreamMachine system.
To assess the data compression and communication protocols between the device and its Android application.

Main Methods:

The study details the software architecture, focusing on data compression and inter-device communication.
An Android application's features, including signal processing parameters, were investigated.
System performance was evaluated using a standard eyes-open/eyes-closed experiment and compared against a laboratory EEG system.

Main Results:

The DreamMachine complies with IFCN standards, offering 24-channel recordings at 250 Hz with EOG and ECG capabilities.
The open-source architecture and Android application facilitate customizable EEG data acquisition.
Performance evaluation demonstrated comparable results to a laboratory EEG system.

Conclusions:

The DreamMachine presents a cost-effective, standards-compliant mobile EEG solution.
Its open-source nature and detailed software architecture support widespread adoption in research and clinical practice.
The system's validated performance indicates its suitability for neurophysiological studies.