2 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes
Athanasios Papastathopoulos-Katsaros, Steven T. Lee, Lin Yao +4
Electroencephalography (EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral features or deep neural networks. Pre…
TD-DPO: Difference-Aware Preference Optimization for Mitigating Sycophancy in Clinical Autism Intervention Dialogue
Shuzhong Lai, Junhong Lai, Chenxi Li +5
The sycophancy of large language models can increase the safety risk in intervention dialogue for autistic children. Supervised fine-tuning can somewhat reduce sycophancy, but rely…
From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset
Junhong Lai, Shuzhong Lai, Yanhao Yu +5
The development of AI-assisted Early Intensive Behavioral Intervention (EIBI) for Autism Spectrum Disorder (ASD) is severely constrained by data scarcity. Furthermore, while Applie…
A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives
Junhong Lai, Jiyu Wei, Lin Yao +1
Electroencephalogram (EEG) signals play a crucial role in understanding brain activity and diagnosing neurological diseases. Because supervised EEG encoders are unable to learn rob…