Nonparametric Supervised Learning for Enhancing BCI Performance

Pei Lun Wu, Cory Stevenson, Li Wei Ko*

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

Improving the function and reliability of brain-computer interfaces (BCIs) is an important factor in facilitating their usage to impact human wellbeing. This research developed a BCI system that utilized nonparametric feature extraction and dimension reduction and supervised learning as a framework for improving accuracy. A BCI experiment using steady-state visually evoked potentials (SSVEP) was conducted as a test basis for our framework. Typical unsupervised learning BCI techniques were tested and found to be improved when harmonic frequencies were included as inputs. Nonparametric weighted feature extraction (NWFE) and physiologically relevant input features were found to improve supervised learning classifiers in our BCI framework, which could outperform the comparable unsupervised methods. This framework presents a novel basis for enhancing BCIs which take into account known physiological information and NWFE to perform better.

原文English
主出版物標題2021 International Automatic Control Conference, CACS 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665444125
DOIs
出版狀態Published - 2021
事件2021 International Automatic Control Conference, CACS 2021 - Chiayi, Taiwan
持續時間: 3 11月 20216 11月 2021

出版系列

名字2021 International Automatic Control Conference, CACS 2021

Conference

Conference2021 International Automatic Control Conference, CACS 2021
國家/地區Taiwan
城市Chiayi
期間3/11/216/11/21

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