Huami Corporation and the University of Science and Technology of China (USTC) Institute of Advanced Technology today announced the establishment of a "Joint Laboratory for Brain and Artificial Intelligence".
They will combine Huami's R&D strengths in the smart wearable field with USTC's research strengths in brain science and artificial intelligence to breakthrough key technologies and build new models for active health.
Wu Feng, assistant to the president of USTC and director of the National Engineering Laboratory for Brain-like Intelligence Technology and Applications, will serve as director of the Joint Laboratory Management Committee.
Chen Xun, Executive Director of the Department of Electronic Engineering and Information Science at USTC and a recipient of the National Outstanding Youth Science Foundation, is the director of the laboratory and is responsible for the daily management of the laboratory.
After the establishment of the joint laboratory, it will carry out non-invasive and invasive brain-computer interface research according to plan, focusing on emotion recognition, sleep health, epilepsy detection, brain semantic analysis and other areas, using EEG signals as information carriers and smart wearable devices to carry out a series of research, highlighting the characteristics of brain-computer intelligence.
The two sides will work together to complete the portable non-invasive EEG prototype system, invasive brain-computer interface experiments, the integration of software and hardware systems for detection devices, and the drawing of whole-brain stress response-related neural loops in animal models.
In addition, the joint laboratory will produce both high-level academic papers and patents on inventions and jointly train graduate students.
Huami and USTC also plan to apply the research results to the medical and health field to build a new model of active health, using smart wearable devices for epilepsy detection, depression, stress recognition, neuromodulation and other applications.
Taking epilepsy detection as an example, the joint laboratory will combine the physiological characteristics of epilepsy brain electrophysiology to propose an efficient epilepsy detection method to further improve the accuracy and robustness of the prediction algorithm, and an accurate epilepsy detection algorithm combined with clinically effective preventive measures can enable patients to substantially avoid possible harm.
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