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我目前是爱丁堡大学的一名在读博士研究生,我的导师是Dr. Yang

此前,我是江苏省产业技术研究院脑机融合智能技术研究所的一名软件算法工程师。我的主要工作包括闭环脑机接口(BCI)算法设计和图像处理相关应用的开发。

更早的时候,我在爱丁堡大学的通信与信号处理专业完成了我的硕士学位, 我的导师是Dr. Elliot J. Crowley。在取得硕士学位之前,我分别获得了利兹大学西南交通大学电子信息工程的工学学士学位。我本科期间的导师是黄德青教授。

我的研究方向包括但不限于:

  • 深度学习
  • 生物医学信号处理
  • 图像处理
  • 脑机接口

在我的空闲时间,我喜欢摄影和户外运动,我还是一个op。


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
  • Tomography
  • Medical Imaging
  • Inverse Problem
  • BCI

In my leisure time, I enjoy photography, outdoor activities, and playing Genshin Impact.

Recent Works

最近工作

Minimal Electrode EEG for BCI Emotion Detection

NNICE 2024

Yuxin Li, Hao Fang, Wen Liu, Chuantong Cheng, Hongda Chen

This study achieves high-accuracy EEG-based emotion recognition (92.8%) using minimal electrode configuration (T7 and T8), advancing efficient and cost-effective BCI device development.

Advancing Brain-Computer Interfaces: An Innovative Optimization Approach for fNIRS-Based Binary Classification

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

ISCEIC 2023

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

EEI 2023

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).

Neural Art meets Edinburgh

M.Sc final project

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

B.Eng final project

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.

BrainTunes

Wechat mini program

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.