Papers
1
Total Citations
268
H-Index
1
About
Lin Shu is a leading researcher in affective computing and brain-computer interfaces, with a primary focus on EEG-based emotion recognition. Her most influential work, "SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG" (2019, 268 citations), introduced a groundbreaking hybrid model that combines a linear EEG mixing model with long short-term memory networks. This framework significantly advanced the field by enabling more accurate and temporally-aware decoding of emotional states from neural signals, directly supporting the development of brain-inspired robots capable of more natural human interaction. Shu’s contributions bridge signal processing and deep learning, offering practical solutions for real-time emotion detection. Her research has been widely cited by peers working on affective human-robot interaction, mental health monitoring, and adaptive intelligent systems. Beyond this seminal paper, her work continues to shape how multi-channel EEG data is modeled for emotional timing and classification. Shu’s achievements underscore her role as a key innovator in making machines more emotionally perceptive, with lasting impact on both computational neuroscience and human-centered AI.
Research Focus
Key Achievements
Top Papers
- 1SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG268 citations · 2019