A working replay demo built around recorded EEG
Raw signals are processed inside Unreal and presented through synchronized spatial views, detailed sensor inspection, and replay.
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
Recorded data helped us imagine a personal, replayable experience.
Our background is in games rather than neuroscience. OpenMIIR—a public dataset recorded while participants listened to and imagined short pieces of music—gave us a practical place to learn what EEG contains and how it needs to be handled.
Playing the matching music alongside the signal was a relatable moment: the recorded visualization no longer felt detached from the activity that produced it. Music is only one example. It suggested that observing EEG alongside meditation, focused work, movement, or another everyday activity could itself be an interesting application.
Just as importantly, recording and revisiting a session is a legitimate use for casual headset owners—not only for scientists. People could return to activities, markers, or changes that caught their attention without needing the experience to make medical claims.
MusicMeditationFocused workMovementVirtual objects
Current state and direction
At a glance.
Connect supported headsets and record live sessions
Live acquisition and local recording extend the processing, visualization, and replay foundation already working today.
An application to explore and a foundation to build upon
A free application for headset owners, paired with an openly accessible Unreal project, reusable code, and practical documentation for developers and researchers.
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 expands how EEG is processed and interpreted, how selected features can enter Unreal’s interactive systems, and how people can explore the results.
Proposed directions
Stronger processing and feedback
More configurable, easier to inspect
- Modular processing beyond the current statistics and five frequency bands
- Calibration for different people and devices
- Signal-quality feedback and practical diagnostics
- Configurable features, thresholds, and temporal conditions
EEG inside interactive experiences
From selected features to Unreal systems
- Unreal-facing values and events derived from carefully selected EEG features
- Mappings for materials, lighting, sound, animation, UI, and gameplay
- Quality gates, thresholds, cooldowns, and visible debugging feedback
- An interface compatible with familiar Unreal input workflows
More ways to explore
Beyond a single brain view
- More mappings across colour, intensity, scale, visibility, and materials
- Presets for observation, demonstration, and experimentation
- Spaces shaped around music, meditation, movement, and virtual objects
- Clearer presentation controls and onboarding
Two outcomes, one foundation
Useful to explore. Practical to build upon.
UnrealEEG is being developed toward two connected outcomes: a free application for people who want to explore their own EEG, and an accessible Unreal foundation that developers and researchers can study, adapt, and build upon. We also intend to share practical documentation and integration findings where third-party licensing and redistribution terms allow.
Intended outcome
For headset owners
Explore EEG without beginning with the pipeline.
An approachable application for seeing activity, adding context, and returning to personal sessions.
- See live activity across a spatial sensor overview
- Select sensors and inspect their signals and derived features
- Add context through markers and presentation choices
- Record sessions locally and revisit them through replay
For developers and researchers
Begin with a working route from signal to experience.
A practical foundation for experimenting without rebuilding the complete path from the start.
- Use processed EEG values and events inside Unreal
- Adapt device inputs, processing, and visual mappings
- Build interactive experiences, experiments, simulations, or research tools
- Inspect and replay behaviour while developing new ideas
The people behind UnrealEEG
Analogica is a UK-based two-person studio.
Bob and Nikolaos 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 VR, robotics, human factors, and game technology, including senior roles at NaturalMotion and Sony. At NaturalMotion, he helped develop and integrate Euphoria and Morpheme technology for AAA studios. He brings project direction and a long-standing interest in turning experimental ideas into usable interactive systems.
Implementation and technical development
Nikolaos Asfis
Nikolaos Asfis is a gameplay programmer with experience at Roll7 and Sony London Studio, where his work included OlliOlli, OlliOlli2, PlayStation VR Worlds, and Blood & Truth. He later co-founded Pixel Magnet and developed Masternoid, a self-published VR arcade game. On UnrealEEG, he leads implementation across Unreal C++, Blueprint, signal processing, visualization, UI, and packaged 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.