Led by Prof. Chin-Teng Lin at UTS’s Australian Artificial Intelligence Institute, the team integrates wearable EEG headsets with fuzzy neural network algorithms to translate brainwave signals into text and commands. They achieved 50% accuracy decoding 24-word sentences and 75% accuracy selecting among four objects by thought, demonstrating potential for hands-free human-machine interaction.

Key points

• Wearable non-invasive EEG headset captures brain signals using surface electrodes.
• Fuzzy neural networks combine IF-THEN rule reasoning with adaptive learning for signal decoding.
• EEG-to-text translation achieves 50% accuracy on 24-word sentence sets.
• Thought-based object selection hits 75% accuracy with four-choice paradigms.
• Real-time online calibration tailors the model to individual users for higher performance.

Why it matters: This demonstration marks a significant step toward everyday non-invasive BCI use, offering a natural interface that could transform human-computer interaction. By achieving meaningful decoding accuracy with wearable EEG and advanced AI, this approach paves the way for accessible assistive technologies and hands-free controls beyond current wearable interfaces.