An Unreal Engine 5 EEG exploration project
Explore brain activity as an interactive experience.
UnrealEEG turns recorded brain signals into a synchronized, replayable experience that can be explored in detail. Live headset support would allow headset owners and developers to experiment with EEG as part of interactive experiences.
How the project began
A chance to work together again led us to explore BCI.
UnrealEEG is being developed through Analogica by its founder, Bob, and Nikos, who together form the project’s two-person team. We met while working at Sony London Studio and stayed in touch after leaving to pursue independent paths.
At London Studio, we contributed to wider SIE efforts exploring PlayStation 5 and PlayStation VR2. Our work focused particularly on controllers and other input features, creating prototypes to investigate how new hardware capabilities could shape the design and experience of future games.
UnrealEEG began when Bob wanted Analogica to pursue an original R&D project alongside its work for AAA studios. We have always shared an interest in how people interact with technology and how new forms of input can shape an experience at a fundamental level. BCI therefore felt like a natural direction to explore.
Coming from games rather than neuroscience, we started with OpenMIIR, a public EEG research dataset. It gave us a practical way to learn how EEG signals are structured and processed while building inside Unreal. That initial exploration became the recorded-data demo and the foundation for the wider project.
Current state and direction
Supported live acquisition and local recording through the same processing, visualization, and replay path
Unreal foundation for EEG research and development, demonstrated through a free application
Inside UnrealEEG
Processing, visualization, and interaction in one real-time application.
The current demo brings local signal processing, synchronized visualization, interaction, and replay together inside Unreal Engine. The values driving its displays can also become inputs to materials, lighting, sound, animation, UI, gameplay, or events. UnrealEEG does not claim to invent EEG-to-Unreal; its value is making more of the complete path coherent and approachable.
Demo today
Signal processing
Local runtime analysis
- Recorded raw EEG processed locally
- Native C++ windowed DSP
- Time-domain statistics
- Five frequency bands
Visualization
Signals made spatial
- Live-updating waveform graphs
- Rolling band-power histories
- Positioned sensor activity
- Optional brain-surface view
Interaction and replay
Explore, inspect, revisit
- Selectable sensors and detail panels
- Free-fly, orbit, and fixed cameras
- Synchronized cues and events
- Packaged Windows demo
Visual opportunityShow overview becoming detailed inspectionView capture prompt
Capture a 6–8 second loop: hover over a sensor, select it, assign it to a side panel, and let its waveform and band histories update. Crop tightly enough for phone viewing.
Proposed development
Bring the existing foundation to supported live devices.
The proposed live system extends the beginning of the path. It does not replace the runtime processing, replay, features, and visuals already working in the demo. Connect supported devices, process incoming signals, record locally, and replay sessions through the same visual path.
How the path grows
Proposed next
EEG headsetContinuous raw signal
Acquire + normalizeChannels, units, timing
Record locally (optional)Keep sessions available for replay
Existing foundation
Process, derive, visualize, and replay
The working UnrealEEG core continues from here.
Live acquisition a new input source for the working system.
Supporting more than one headset is an important part of UnrealEEG’s direction. Within the available budget, we plan to build that support progressively, beginning with devices that help establish a reliable common input while testing meaningfully different capabilities and access models. Because devices vary in how they expose live raw data and in the software required to use it, each integration may take a different route before reaching the shared UnrealEEG foundation.
OpenBCI and Muse are examples of complementary hardware targets we would like to explore. OpenBCI’s open approach, range of devices, flexible sensor configurations, and active community make it valuable for testing UnrealEEG across different setups. Muse represents a different kind of target: an approachable consumer headset with a small sensor set and a history of research use. It could provide a practical baseline for understanding what the application can offer with minimal hardware before moving toward richer configurations.
Visual opportunityAnimate this live path progressivelyView capture prompt
Reveal the proposed acquisition and live-session path first, then show it joining the summarized existing foundation. Preserve cyan for existing and magenta for proposed.
Extend the foundation in meaningful ways.
Live acquisition is the enabling step. From there, the work would focus on three practical areas.
Proposed directions
Extract useful EEG features
The existing frequency-band and signal analysis provides the base for a broader processing toolkit. This would include configurable filters, signal-quality checks, and calibration for each user and session. Analysis could also be linked to timed cues inside Unreal. This could support experiments with changing brain rhythms, responses to stimuli, imagined movement, and other established EEG patterns. The raw signal and every processing stage would remain accessible.
Turn EEG into Unreal input
Colour, light, and scale already respond to signal intensity and dominant frequency bands. Configurable mappings would open those connections to C++ and Blueprints. Developers could choose which sensors and features to use, then adjust how the values respond. They could also decide when a changing value should trigger an event. The results could then drive materials, lighting, sound, animation, UI, or gameplay.
Build richer 3D EEG views
The positioned sensors and brain model provide the starting point for a richer spatial view. Different sensor displays and smoother activity maps based on the positioned scalp sensors could make the signal easier to explore. Colour, light, scale, and materials could offer greater control over how the data is presented. Users could focus on selected sensors or isolate parts of the model. They could also compare different features and moments, with additional graphs available for closer analysis.
One foundation, two communities
Bringing researchers into Unreal, and Unreal developers into BCI.
Intended outcome
For researchers
Build experiments in Unreal without starting from scratch.
UnrealEEG gives researchers a practical starting point for creating EEG experiments and interactive research tools in Unreal, so they can focus on the question, experience, and feedback rather than assembling the complete technical pipeline first.
For Unreal developers
Work with EEG without first becoming a signal-processing specialist.
UnrealEEG makes processed EEG available as a source of input that developers can understand, inspect, and connect to the Unreal systems they already work with, opening a more approachable route into BCI experimentation.
The people behind UnrealEEG
Analogica is a UK-based two-person studio.
Bob and Nikos met at Sony London Studio while exploring how prospective PlayStation hardware and input features might support new forms of gameplay. UnrealEEG continues that shared interest in unfamiliar input technology.
Project direction and technical perspective
Bob Dowland
Bob Dowland is the founder of Analogica and a specialist in real-time animation, physics, and simulation. His career spans games, VR, robotics, human factors, and scientific visualization. At NaturalMotion, he was one of the founding engineers behind Euphoria Network and helped develop and integrate Euphoria and Morpheme technology for AAA studios including Rockstar and LucasArts. At Sony London Studio, he conceived and developed systems for real-time interaction between animated hands and physically simulated VR environments. On UnrealEEG, he provides project direction and brings a long-standing focus on turning experimental ideas into usable interactive systems.
Implementation and technical development
Nikos Asfis
Nikos Asfis is a multidisciplinary game developer who combines extensive programming experience with hands-on involvement in game design and wider development. As Lead Programmer at Roll7, he led development on OlliOlli and OlliOlli2; the original won the 2015 BAFTA for Sport. At Sony London Studio, he worked on PlayStation VR Worlds and Blood & Truth, as well as prototypes exploring prospective PlayStation hardware and input features. He later co-founded Pixel Magnet, taking on responsibilities across multiple areas of development for the VR title Masternoid. On UnrealEEG, he brings the same breadth to Unreal C++, signal processing, visualization, UI, and application development.
Explore UnrealEEG
The demo is working. The larger idea is still worth exploring.
UnrealEEG is self-directed R&D developed by Analogica. The next milestone is to add supported live acquisition and develop the existing foundation into a free application and reusable Unreal project.