I am currently a PhD student at the Institute for Imaging, Data and Communications (IDCOM), the School of Engineering, the University of Edinburgh. My principal supervisor is Dr. Yang.
Prior to this, I was an algorithm engineer at JITRI Brain-Machine Fusion Intelligence Institute, Jiangsu Industrial Technology Research Institute. I was responsible for brain-computer interface algorithm development.
Before that, I completed my M.S. in Signal Processing and Communications at the University of Edinburgh, advised by Dr. Elliot J. Crowley. Prior to my MSc, I pursued a BEng in Electronic and Electrical Engineering from the University of Leeds and Southwest Jiaotong University. My undergraduate supervisor was Prof. Huang.
My research interests include (but are not limited to):
- Deep learning
- Biomedical signal processing
- Image processing
- BCI
In my leisure time, I enjoy photography, outdoor activities, and playing Genshin Impact.
Recent Works
最近工作
Journal of Neural Engineering (under review)
Yuxin Li, Hao Fang, Wen Liu, Chuantong Cheng, Hongda Chen
Achieving high subject-independent accuracy in online classification is an important part in the field of functional near-infrared spectroscopy (fNIRS) based Brain-computer Interface (BCI), especially with a reduced number of channels. However, it is often not possible to achieve both of them. In this paper, we proposed a novel feature extraction scheme for fNIRS dataset to improve the subject-independent accuracy. Additionally, we proposed a channel selection algorithm to locate the ROI (Region of Interest) channels, reducing the number of channels required for such classification tasks.
V-SWIR-IF: Visible and Short-Wave Infrared Image Fusion
Hao Fang, Guanjie Xu, Gaomin Su, Chuantong Cheng
An image fusion algorithm for fusing visible light and short-wave infrared (SWIR) images. This work was supported by the National Key R&D Program of China (Grant No.2021YFB3601201).
Enabling Transcranial Electrical Stimulation via STI01: Experimental Simulations and Hardware Circuit Implementation
Guanjie Xu, Gaomin Su, Hao Fang, Yue Li
A novel utilization of a chip, STI01, developed in China and traditionally used for muscle stimulation, to meet the demands of transcranial electrical stimulation (tES).
Hao Fang, Guanjie Xu, Hui Zhong, Gaomin Su
EEG-based BCI product for emotion recognition with only two electrodes.
Hao Fang
I employed deep learning algorithms such as fast style transfer, VQGAN-CLIP, DALLE-2, and Stable Diffusion to generate amazing artworks in various styles that depict the landscapes in Edinburgh. Besides, I set up an online art gallery on GitHub Pages to display these art creations.
Real-time Face Covering Detection Based on Deep Learning
Hao Fang
A three-class object detection problem using the YOLOv5 deep learning algorithm. The aim was to check if individuals wear their masks correctly. This real-time detection system has significant potential for practical use and commercial value during the pandemic.
Hao Fang
A WeChat mini-program I developed for our BCI device. You can access my mini-program by searching for "BrainTunes" in the WeChat client.