摘要
Decoding motion velocity from electroencephalography (EEG) signals is important for brain-computer interface (BCI) research. However, no studies explore how to decode the velocity of complex motion. In this paper, we apply extreme learning machine (ELM) to explore how to decode the velocity of complex motion from EEG signals. We design a new experimental paradigm and analyze the effects of the number of hidden neuron nodes and frequency band on the decoding performance. This work lays a foundation of building accurate motion decoders from EEG signals to develop the BCI-based prostheses and rehabilitation systems.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | Proceedings - 2017 Chinese Automation Congress, CAC 2017 |
| 出版商 | Institute of Electrical and Electronics Engineers Inc. |
| 页 | 4901-4905 |
| 页数 | 5 |
| ISBN（电子版） | 9781538635247 |
| DOI | |
| 出版状态 | 已出版 - 29 12月 2017 |
| 活动 | 2017 Chinese Automation Congress, CAC 2017 - Jinan, 中国 期限: 20 10月 2017 → 22 10月 2017 |
出版系列
| 姓名 | Proceedings - 2017 Chinese Automation Congress, CAC 2017 |
|---|---|
| 卷 | 2017-January |
会议
| 会议 | 2017 Chinese Automation Congress, CAC 2017 |
|---|---|
| 国家/地区 | 中国 |
| 市 | Jinan |
| 时期 | 20/10/17 → 22/10/17 |
指纹
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Su, L., Bi, L., Fei, W., & Lian, J. (2017). Motion velocity estimation from electroencephalography signals with extreme learning machine. 在 Proceedings - 2017 Chinese Automation Congress, CAC 2017 (页码 4901-4905). (Proceedings - 2017 Chinese Automation Congress, CAC 2017; 卷 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC.2017.8243647