- 期刊
Semi-supervised Learning Based EEG Detection Approach for Rehabilitation Engineering
摘要
A semi-supervised learning based EEG signal detection method was studied in this paper. The feature engineering system of this paper was established, which contains novel AutoEncoders mapping features. The optimal channel combination for all subjects was determined to improve recognition accuracy by ReliefF algorithm and recursive feature elimination. What's more, the semi-supervised learning method based on pseudo-labelling was introduced to the character recognition method, in which the training samples were dynamically reorganized and updated, so that the proposed method could complete the symbol recognition with limited number of training samples. Based on the features extacted and the optimal channel combination, the recognition accuracy of the character recognition method can reach up to 100%.
延伸閱讀
- Lin, C. F., & Wang, B. S. (2011). A 2D Chaos-Based Visual Encryption Scheme for Clinical Eeg Signals. Journal of Marine Science and Technology, 19(6), 666-672. https://doi.org/10.6119/JMST.201112_19(6).0011
- Lan, T., Erdogmus, D., Adami, A., Mathan, S., & Pavel, M. (2007). Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG. Computational Intelligence and Neuroscience, 2007(), 299-310. https://doi.org/10.1155/2007/74895
- 彭彥鈞（2020）。Recognizing EEG Data Using Random Forest and Front End Interactive Implementation〔碩士論文，國立交通大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0030-1504202111220963
- 洪瑋敏（2019）。Deep Learning Methods for the Suppression of Motion Artifact in ECG Signals〔碩士論文，國立交通大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0030-0306202016392619
- Kao, C. M. (2015). Mental Effort Detection Using EEG Data in E-learning Contexts [master's thesis, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-0312201510272855