@inproceedings{f16100d4e5e54ae58a359097117dc4e9,
title = "Live Demonstration: Real-time EEG-based Affective Computing Using On-chip Learning Long-term Recurrent Convolutional Network",
abstract = "An edge artificial intelligence (AI) affective computing system based on electroencephalogram (EEG) will be demonstrated for multi-class emotional classification. It's composed of a dry electrode EEG headset, RISC-V feature extraction processor, long-term recurrent convolutional network (LRCN) on-chip platform, and graphical user interface (GUI). The LRCN platform is implemented with a TSMC 16-nm FinFET technology chip for efficient edge AI application included training and acceleration. Bluetooth 2.1 modules are deployed to construct a complete wireless edge-AI system from front-end to back-end. It takes 350 ms to identify and demonstrate one emotion state from the EEG headset front-end to the GUI display back-end.",
author = "Yang, {Cheng Jie} and Li, {Wei Chih} and Wan, {Meng Ting} and Fang, {Wai Chi}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 ; Conference date: 06-10-2021 Through 09-10-2021",
year = "2021",
doi = "10.1109/BioCAS49922.2021.9645030",
language = "English",
series = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",
address = "美國",
}