The BRAIN lab concentrates on multiple (bio) sensory modality feedback and machine learning for complex sensory signal processing and integration, adaptive neural motor control, embodied artificial intelligence, and their applications in human-machine interaction and rehabilitation domains. We also aim to develop “BRAIN technology” based on bio-inspired robotics and neural engineering. The technology will be used for advanced human-machine interaction
How can brain-like mechanisms be developed and realized on artificial systems so they can perform multiple complex functions as biological living systems? To address this fundamental question, we employ a bio-inspired approach to develop brain-like mechanisms for adaptive motor control and autonomous learning of embodied multi-sensorimotor robotic systems. The developed mechanisms (BRAIN technology) are adaptive and flexible, which can be transferred to application areas like human-machine interaction, brain-machine interface, and rehabilitation.
An investigation of closed-loop EEG-based Brain Computer Interface (BCI) helps bridge the gap in communication between man and machine. Understanding patterns of brain activity from basic neuroscience research will help engineering to optimize the BCI system that could be applied in the real-world environment. Advanced signal processing techniques and pattern recognition methods such as machine learning, artificial neural network, and deep learning can improve the efficiency of BCI by increasing information transfer rate and accuracy. Ultimately, research based on BCI could lead to the development of novel tools used in both clinical and healthy populations.