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IFEN Roads - Neurofeedback Game 3D
IFEN Roads - Neurofeedback Game 3D
Regular price
247,16 €
Regular price
Sale price
247,16 €
Unit price
per
Including VAT
In an exciting race setting, the trainee competes wheel-to-wheel against other vehicles. The goal is to successfully master each track. The game offers various tracks, vehicle options, and modes, ensuring that each race remains varied, challenging, and entertaining.
Positive Feedback: The car accelerates and participates in the race.
Negative Feedback: The car stops or is very slow.
Adaptive difficulty of AI opponents: As the trainee improves, the AI opponents also become more competitive. This increases the difficulty of the race, keeping the gameplay exciting and competitive.
Performance presentation
Progress
Adjustment
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In stock
Delivery time approx. 1-4 working days
Transparent VAT regime for EU business customers
Transparent VAT regime for EU business customers
All prices include 19% German VAT. For business customers from the EU, VAT will be automatically removed at checkout after entering a valid VAT ID number (reverse charge).
- EEG neurofeedback compatible
- Continuous Feedback
- Positive Feedback
- Negative Feedback
- Non-overstimulating game environments (e.g. graphics, audio)
- In-game customization options (e.g. music, levels, modes, environments, character selection)
- Motivational design across repeated training sessions
- Cross-session progress tracking
- Customizable difficulty levels
- Personalized threshold calibration
- Suitable for all age groups (children, adolescents, and adults)
- Adjustable screen brightness and volume
- Real-time feedback signaling (latency < 350 ms)
- AI-based computer opponents
- Clearly defined game objectives
- Multilingual user manuals (English, German, Spanish, Romanian)
- Immersive, user-centric gaming experience
How does the Reward Prediction Error (RPE) support neurofeedback training in IFEN Roads?
IFEN Roads is a neurofeedback racing game based on the Reward Prediction Error (RPE) by linking real-time brain-state criteria with immediate performance and reward access. Players compete against AI opponents, and their car only accelerates and competes when the criteria are met; if they are not, the race continues, but the car remains stationary or very slow. This creates a tight learning loop based on reinforcement updates. A live criteria bar makes the contingency explicit, while dimmer and sound modes add multimodal feedback by removing screen dimming and increasing the sound when the criteria are met, and decreasing both when they are not. Progress is tracked in a leaderboard (mode, difficulty, time, rank, date), and the more consistently players meet the criteria, the more challenging the AI competition can become, supporting structured shaping over repeated sessions.
Neurofeedback is understood as a self-regulation process based on reinforcement learning, in which this is acquired through contingent feedback.
Motivation, salience of the reinforcer, and sustained engagement are considered important moderators of learning success. Since the subjective motivational value of feedback can vary between individuals and decrease with repeated exposure, the adaptive design of feedback environments is discussed as an approach to maintain effective reinforcement.
Against this background, offering multiple feedback or game options can be understood as a theoretically sound strategy to support engagement, reduce habituation effects, and maintain the reinforcing value of feedback across training sessions, thereby potentially promoting learning stability and the transfer of effects (Schwartz & Andrasik, 2016; Enriquez-Geppert et al., 2017; Lubianiker et al., 2022; Sitaram et al., 2024).
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