DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Sang Wan | - |
dc.contributor.advisor | 이상완 | - |
dc.contributor.author | Kim, Myeong Hyeon | - |
dc.date.accessioned | 2023-06-23T19:30:48Z | - |
dc.date.available | 2023-06-23T19:30:48Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032728&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308728 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2023.2,[iv, 42 p. :] | - |
dc.description.abstract | Recent studies have revealed that decoding underlying learning strategies helps to decode the corresponding behavior or movement. This is because high-level cognitive states such as intention, learning strategies, or prediction errors (PEs) always precede and underlie every form of movement. However, Brain-Computer Interface (BCI), which serves as a communication channel between the brain and the machine, has focused on motor function-related EEG signals. Because motor function-related EEG signal delivers information only about which and how muscles are moved, it may not suitable for decoding high-level cognitive functions or complex movements. Related works have relied on a limited experimental environment, such as a 2-stage Markov Decision Task (MDT), making it hard to achieve realistic PE signals. To settle this issue, this study aims to decode the high-level cognitive state PE signal in a realistic environment. Specifically, we propose a novel realistic task paradigm with a high variability of PEs and implement the EEG-based PE decoder. As a proof of concept, we tested our model on the 2-stage MDT dataset obtained in the previous study and achieved an accuracy of 61.3 % for SPE and 58.4 % for RPE. Finally, we achieved an accuracy of 97.4 % for RPE in the realistic environment we suggested here. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Prediction error▼aBrain-computer interface▼aRealistic environment▼aPE decoder▼aEEG decoder | - |
dc.subject | 예측 오차▼a뇌-컴퓨터 인터페이스▼a현실적인 실험 환경▼a예측 오차 분류기▼aEEG 분류기 | - |
dc.title | Prediction error decoding from EEG signals in a realistic environment | - |
dc.title.alternative | 현실적인 실험 환경에서 취득한 EEG 신호로부터의 예측 오차 분류에 대한 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :바이오및뇌공학과, | - |
dc.contributor.alternativeauthor | 김명현 | - |
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