TY - GEN
T1 - Importance of Reliable EEG Data in Motor Imagery Classification
T2 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
AU - Lee, Seho
AU - Kim, Young Tak
AU - Hwang, Seung Ouk
AU - Kim, Hakseung
AU - Kim, Dong Joo
N1 - Funding Information:
through the National Research Foundation of Korea(NRF) funded by of Science and ICT (NRF-2019M3C1B8077477). *Corresponding author
Funding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface); Convergent Technology R&D Program for Human Augmentation
PY - 2020/2
Y1 - 2020/2
N2 - Brain-computer interface (BCI) has been widely used to predict the intention of users in motor imagery-based (MI-based) task. Although the overall MI classification accuracy has been largely enhanced from previous efforts, applying MI-BCI to the so-called BCI-illiterate subjects remains as an unsolved problem. This study proposed a physiological approach for improving MI-BCI performance, by measuring the baseline attention level estimated by coefficient F from the electroencephalogram (EEG) band-activities. In this endeavor, a total of 9 MI-EEG recordings were retrieved from an open BCI dataset. A measure of attention level was calculated for each trial to select high attention trials. High attention trial-based machine learning model showed higher MI classification performance (median accuracy = 62.50% (interquartile range (IQR) = 55.21-82.29%)) than the conventional approach (median accuracy = 57.64% (IQR = 54.17-62.50%)) with statistical significance (Wilcoxon rank sum test, p = 0.037). This study found that machine learning models trained from high attention trials yield improved classification accuracy to the models derived from total trial regardless of both BCI illiterate and literate.
AB - Brain-computer interface (BCI) has been widely used to predict the intention of users in motor imagery-based (MI-based) task. Although the overall MI classification accuracy has been largely enhanced from previous efforts, applying MI-BCI to the so-called BCI-illiterate subjects remains as an unsolved problem. This study proposed a physiological approach for improving MI-BCI performance, by measuring the baseline attention level estimated by coefficient F from the electroencephalogram (EEG) band-activities. In this endeavor, a total of 9 MI-EEG recordings were retrieved from an open BCI dataset. A measure of attention level was calculated for each trial to select high attention trials. High attention trial-based machine learning model showed higher MI classification performance (median accuracy = 62.50% (interquartile range (IQR) = 55.21-82.29%)) than the conventional approach (median accuracy = 57.64% (IQR = 54.17-62.50%)) with statistical significance (Wilcoxon rank sum test, p = 0.037). This study found that machine learning models trained from high attention trials yield improved classification accuracy to the models derived from total trial regardless of both BCI illiterate and literate.
KW - BCI illiteracy
KW - attention
KW - brain computer interface
KW - electroencephalography
KW - motor imagery task
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U2 - 10.1109/BCI48061.2020.9061647
DO - 10.1109/BCI48061.2020.9061647
M3 - Conference contribution
AN - SCOPUS:85084040016
T3 - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
BT - 8th International Winter Conference on Brain-Computer Interface, BCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 February 2020 through 28 February 2020
ER -