Pervasive Artificial Intelligence Research (PAIR) Labs

Home care

Pervasive Artificial Intelligence Research (PAIR) Labs

Home care

NCU – Professor Kuo-Kai Shyu

Professor Kuo-Kai Shyu Principal Investigator

Research :

Development of Artificial Intelligence–based Brain Computer Interface
(Co-PI : Prof. Po-Lei Lee)

Core Technologies

We aim to build EEG databases and apply artificial intelligence (AI) to clinical, healthcare, and educational applications. With the development of wearable EEG system, EEG data under different circumstances can be collected. AI systems built on the cloud or edge computing are used to recognized users’ intentions and produce feedbacks to achieve brain-machine interaction, called AI-based brain computer interface (BCI).

Applications

Brain Computer Interface, Clinical Application, Healthcare Application, Educational Application

Technology Transfer

1.The development of a novel wearable medical-level EEG recorder
2.The real-time emotion identified of AI-based EEG analysis
3.The control of umanned aerial vehicle via EEG signals
4.The real-time identification of learning cognivie load and learning attention

Proposal for Industry-Academia Collaboration

1.The prediction of post-stroke epilepsy
2.A novel development of brain computer interface-the hybrid of EEG recorder and inertial measurement unit.
3.The prediction technology of EEG motor imagery
4.The AI-based prediction and identification of rehabilitation exercise
5.The development of the AI-based analysis technology on the non-invasive blood glucose measurement
6.The development of the AI-based analysis technology on the cuffless blood pressure

Technologies with Potential for Technology Transfer or Industry-Academia Collaboration

1.Tailored transcranial brain stimulation strategies based on precision medicine for stroke rehabilitation
2.The fMRI feature analysis of depression
3.The AI-based prosthetic-orthotic system
4.The home-care system of the AI-based prosthetic-orthotic system
5.The learning emotion-care system
6.The learning cognitive load of the AI-based feedback technology