LBW1210: Guess or Not? A Brain-Computer Interface Using EEG Signals for Revealing the Secret …
LBW1210: Guess or Not? A Brain-Computer Interface Using EEG Signals for Revealing the Secret behind Scores
Tao Xu, Yun Zhou, Yuhan Wang, Zichen Zhao, Shiqian Li
CHI '19: ACM CHI Conference on Human Factors in Computing Systems
Session: Poster Rotation 1
Abstract
Now examinations and scores serve as the main criterion for a student's academic performance. However, students use guessing strategies to improve the chances of choosing the right answer in a test. Therefore, scores do not reflect actual levels of the student's knowledge and skills. In this paper, we propose a brain-computer interface (BCI) to estimate whether a student guesses on a test question or masters it when s/he chooses the right answer in logic reasoning. To build this BCI, we first define the "Guessing'' and employ Raven's Progressive Matrices as logic tests in the experiment to collect EEG signals, then we propose a sliding time-window with quorum-based voting (STQV) approach to recognize the state of "Guessing'' or "Understanding'', together with FBCSP and end-to-end ConvNet classification algorithms. Results show that this BCI yields an accuracy of 83.71% and achieves a good performance in distinguishing "Guessing'' from "Understanding''.
DOI:: https://doi.org/10.1145/3290607.3312904
WEB:: https://chi2019.acm.org/
LBW1210: Guess or Not? A Brain-Computer Interface Using EEG Signals for Revealing the Secret behind Scores
Tao Xu, Yun Zhou, Yuhan Wang, Zichen Zhao, Shiqian Li
CHI ’19: ACM CHI Conference on Human Factors in Computing Systems
Session: Poster Rotation 1
Abstract
Now examinations and scores serve as the main criterion for a student’s academic performance. However, students use guessing strategies to improve the chances of choosing the right answer in a test. Therefore, scores do not reflect actual levels of the student’s knowledge and skills. In this paper, we propose a brain-computer interface (BCI) to estimate whether a student guesses on a test question or masters it when s/he chooses the right answer in logic reasoning. To build this BCI, we first define the “Guessing” and employ Raven’s Progressive Matrices as logic tests in the experiment to collect EEG signals, then we propose a sliding time-window with quorum-based voting (STQV) approach to recognize the state of “Guessing” or “Understanding”, together with FBCSP and end-to-end ConvNet classification algorithms. Results show that this BCI yields an accuracy of 83.71% and achieves a good performance in distinguishing “Guessing” from “Understanding”.
DOI:: https://doi.org/10.1145/3290607.3312904
WEB:: https://chi2019.acm.org/
