The NeuroTech research group of the Advanced Institute of Engineering and Technology (AVITECH) was founded in April 2019, with the vision of becoming a leading interdisciplinary research center in neurotechnology, capable of developing, training, and transferring knowledge, advanced technologies, and applications based on neuroscience and engineering principles that can impact practices in medicine, healthcare, and education.

Core members

  1. Dr. Le Vu Ha, Group leader
  2. Dr. Phung Manh Duong
  3. Prof. Quang Ha, International advisor

Brain-machine interface (BMI)

The research is aimed at developing an EEG-BMI-based interactive VR systems capable of real-time processing and analyzing EEG signals in order to generate audio-visual neurofeedback (sounds and 3D graphics), to be used as a platform for data acquisition, research, and development of computerized systems to assist diagnosis and treatment of brain disorders like depression and dementia.

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

  1. Le Trung Thanh, Nguyen Thi Anh Dao, Nguyen Viet Dung, Nguyen Linh Trung, and Karim Abed-Meraim. Multichannel EEG epileptic spike detection by a new method of tensor decomposition. IOP Journal of Neural Engineering, 17(1):016023, January 2020.
  2. Nguyen Thi Anh-Dao, Le Trung Thanh, Viet-Dung Nguyen, Nguyen Linh-Trung, and Ha Vu Le. New feature selection method for multi-channel EEG epileptic spike detection system. VNU Journal of Science: Computer Science and Communication Engineering, 35(2):47–59, 2019.
  3. Le Thanh Xuyen, Le Trung Thanh, Nguyen Linh Trung, Tran Thi Thuy Quynh, and Nguyen Duc Thuan. EEG source localization: A new multiway temporal-spatial-spectral analysis. In 2019 NAFOSTED Conference on Information and Computer Science (NICS), Hanoi, Vietnam, December 2019.
  4. Nguyen Thi Anh-Dao, Le Trung Thanh, Nguyen Linh-Trung, and Ha Vu Le. Nonnegative tensor decomposition for EEG epileptic spike detection. In NAFOSTED Conference on Information and Computer Science (NICS), pages 196–201, Hanoi, Vietnam, November 2018. [Best paper award]
  5. 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.
  6. 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.

Other information

Contact: Dr. Le Vu Ha, AVITECH