Papers
1
Total Citations
9
H-Index
1
About
Xinjie He is a rising researcher at the forefront of brain-computer interface (BCI) technology, with a focused expertise in steady-state visual evoked potential (SSVEP) classification. His most notable contribution is the development of a Squeeze and Excitation-Based Multiscale CNN, a novel deep learning architecture that significantly enhances the accuracy of recognizing user intentions from visual stimuli. This work, published in 2024 and already garnering 9 citations, directly addresses the critical challenge of reliable BCI control for real-world applications. By improving SSVEP classification, He’s research is paving the way for intuitive, non-invasive control of Internet of Things (IoT) devices, with promising implications for smart healthcare and smart home systems. His approach elegantly combines multi-scale feature extraction with channel-wise attention mechanisms, setting a new benchmark for performance in the field. As a young scholar, Xinjie He is establishing himself as a key innovator in making BCI technology practical and accessible for everyday assistive and smart environments.
Research Focus
Key Achievements
Top Papers
- 1