• Advanced Institute of Engineering and Technology (AVITECH)

  • Neurotechnology

    Established in 2019, the Neurotech research group of the Advanced Institute for Engineering and Technology (AVITECH) is aimed at becoming an interdisciplinary research center for neurotechnology, capable of conducting research, development, transfer, and training of neurological technologies for medical, healthcare, and educational applications.


    Dr. Lê Vũ Hà, group leader

    Dr. Phùng Mạnh Dương

    Prof. Quang Ha, advisor

    Brain-machine interface (BMI)

    Research and development of interactive systems based on EEG-BMI and virtual reality (VR) technologies. Such systems are capable of acquiring, processing, and analyzing EEG signals from users, and generating neurofeedback in the forms of 3D graphics and sounds, to be used as tools for data acquisition and analysis in brain disorder research, as well as platforms for development of computer-based diagnosis and therapy of brain disorders like depression, dementia, etc.

    EEG signal analysis for epilepsy

    Epilepsy is a set of chronic neurological disorders, which can be characterized by seizures and epileptiforms. Epileptic seizures result from abnormal, excessive or hyper synchronous neuronal activity in the brain. Epileptiforms are waveforms related to epilepsy, such as spikes, sharp waves and spike-wave complexes and occur before or after a seizure. Scalp electroencephalogram (EEG), which is the recording of electrical activity of the brain, measures voltage fluctuations resulting from ionic current flows within the neurons of the brain by using electrodes placed on the scalp. Among different tools for epilepsy analysis, scalp EEG remains the most accessible method. Despite limited spatial resolution, EEG continues to be a valuable tool for research and diagnosis, especially when millisecond-range temporal resolution is required. In the procedure of epilepsy diagnosis, automatic spike detection is important because it can provide much information, such as spike density and patient syndrome. Much effort has been spent on spike detection over the last 40 years. While manual spike detection via visual identification by neurologists is very time consuming, state-of-the-art automatic spike detection remains difficult for a number of reasons. In this research direction, we develop software systems to detect epileptic spikes automatically.

    Selected Publications

    Nguyen Thi Anh-Dao, Nguyen Linh-Trung, Nguyen Van-Ly, Tan Tran-Duc, Hoang-Anh The Nguyen, and Boualem Boashash. A multistage system for automatic detection of epileptic spikes. REV Journal on Electronics and Communications, 8(1–2):1–13, January–June 2018.

    Le Thanh Xuyen, Le Trung Thanh, Dinh Van Viet, Tran Quoc Long, Nguyen Linh-Trung, and Nguyen Duc Thuan. Deep learning for epileptic spike detection. VNU Journal of Science: Computer Science and Communication Engineering, 33(2):1–13, December 2017.