In-Nea Wang
Papers
1
Total Citations
5
H-Index
1
About
In-Nea Wang has made significant contributions to the field of brain-computer interfaces (BCI), with a particular focus on motor imagery classification for rehabilitation. Her most cited work, "Recurrent convolutional neural network model based on temporal and spatial feature for motor imagery classification" (2019), addresses a critical challenge in BCI-based rehabilitation for paralyzed patients. By developing a novel deep learning architecture that integrates both temporal and spatial features—rather than treating spatial and spectral features independently—Wang advanced the accuracy and reliability of motor imagery decoding. This work, with 5 citations, demonstrates her ability to bridge computational modeling with practical clinical applications. Wang's research sits at the intersection of neural engineering and artificial intelligence, aiming to improve quality of life through more intuitive BCI systems. Her approach highlights the importance of holistic feature extraction in neural signal processing, offering a pathway toward more responsive and effective assistive technologies. As BCI research continues to evolve, Wang's contributions remain foundational for students and researchers seeking to develop robust, real-time motor imagery classifiers.
Research Focus
Key Achievements
Top Papers
- 1