Development of Intelligent Neurofeedback Training and Sleep Enhancement System

Principal Investigator |Sheng-Fu Liang, Professor of Dept. CSIE, NCKU
Co-Principal Investigator |
Fu-Zen Shaw, Distinguished Professor of Psychology Dept, NCKU
Zong-Hua Lu, Doctor of Psychiatry Dept, NCKU Hospital
Yuan-Hong Wang, Associate researcher of RCADA, NCU
Chih-En Kuo, Assistant Professor of Automatic Control Engineering Dept, FCU
Project Intro
Among the residual symptoms in remitted depressive or anxiety disorder, most patients paid much concerned about the sleep disturbance. The sleep disturbance not only influence the cognitive function but also increase the relapse rate of depression or anxiety. However, for chronic insomniac patients with depression or anxiety, the common antidepressants influence the sleep architecture and sleep quality. Also, most hypnotics could not fulfill all the condition for sleep induction, sleep maintenance, not influencing sleep architecture, not bothering daily life function and non-dependence tendency. This project integrates experts with rich experiences in artificial intelligence, edge computing, neurosensing, information engineering, neural science and engineering, psychiatry, and sleep medicine, to develop the intelligent neurofeedback training and sleep quality assessment system for sleep enhancement that can be applied to normal people and patients with insomnia. Through one-month non-pharmacological neurofeedback training, it is expected to show effectiveness in treating insomnia by using neuronal potential activity as signals to adjust the brain function. The performances of neurofeedback training and the changes of brain responses between normal people and patients with insomnia will be compared and evaluated by neuroimaging technology. For the AI developing, edge-computing-based sleep analysis and neurofeedback methods will be developed from the small data size at the beginning of the experiments by combining expert knowledge and intelligent inference System. Meanwhile, the sleep quality assessment system for cloud computing will also be developed based on huge database and deep learning technology. The applicability of our system in sleep monitoring and enhancement will also be evaluated for 6-month follow-up study. It is expected that the developing devices and technology will be mature for commercialization based on our successful experience in technology transfer. For the industry, the developing technologies will provide new opportunity to develop next-generation innovative wearable products. For the technical breakthrough, the success of this project will enable the neurofeedback training to be used in clinics and day-to-day living and extend the applications of AI and wearable devices from assessment to performance enhancement. In terms of society, healthy persons as well as insomnia patients will be able to achieve better quality of life due to the innovative neurosensing and neurofeedback training technology.
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System Capabilities
  • Nervous System feedback training-
    Wearable Autonomic Using brain-computer interface technology, combined with the design of a comfortable brain wave sensing device. At this stage, users can gauge their current brain training state from the visual stimulation of the APP via mobile device; the APP can prompt users to adjust their training strategy, consolidate/strengthen the training effect, and achieve long-term improvements of insomnia.
  • At Home Sleep Assessment-
    Patients can wear smart eye masks and a watch that records sleep behavior by using a smart automatic sleep stage analysis software to achieve the needs of home sensing and long-term tracking. The in-home system and expert analysis has over 85% consistency, which solves the difficulty of waiting on a vacancy in the Sleep Center, the inability to track at home for a long time, the existing wearable devices not accurately providing a complete sleep structure, and ultimately meets the urgent needs of the patient’s home sleep quality assessment and tracking.
  • Cloud data collection and medical assistance –
    With patient consent, physicians may access the patient’s sleep data, including brainwave data for clinical sleep diagnosis, automatic sleep interpretation staging reports, sleep indicators, and brainwave overview and usage during training strategies, which can be used as a reference for the adjustment of insomnia treatment, which in turn accelerates the cure of chronic insomnia in patients.
Cooperating Partners
  • Hokkaido University Research Institute for Electronic Science
  • NCKU Hospital Psychiatry Dept.
  • NCKU Hospital Sleep Center
Lab Link
NCBCI
Contact info
Tel:06-275-7575 ext.62549
Mail:sfliang@mail.ncku.edu.tw