TY - GEN
T1 - Application of Machine Learning Algorithm for Mental State Attention Classification Based on Electroencephalogram Signals
AU - Suwida, Katon
AU - Hidayati, Shintami Chusnul
AU - Sarno, Riyanarto
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Technological developments provide a new work environment where the human role is reduced to that of a passive observer. Risk in the workplace might result from a person's incapacity to maintain attention and concentration while doing passive control activities. Passive brain-computer interface (BCI) can be used to monitor mental attention status in humans (focused, unfocused, and drowsy) using electroencephalogram (EEG) signals. This study proposes using BCI to detect the level of mental attention status based on EEG signals using machine learning. In designing the architecture of this study, the EEG signal data has been decomposed by the Discrete Wavelet Transform (DWT) decomposition process with 4 decomposition levels and using the Daubechies family (dB). The signal decomposition results are extracted using several statistical features, which are then used for machine learning model features. The Xtreme Gradient Boost (XGBoost) algorithm was used to perform the classification task. The XGBoost model produced accuracy results of 99% (best) on individual subject tests and 98% (average) on all subjects.
AB - Technological developments provide a new work environment where the human role is reduced to that of a passive observer. Risk in the workplace might result from a person's incapacity to maintain attention and concentration while doing passive control activities. Passive brain-computer interface (BCI) can be used to monitor mental attention status in humans (focused, unfocused, and drowsy) using electroencephalogram (EEG) signals. This study proposes using BCI to detect the level of mental attention status based on EEG signals using machine learning. In designing the architecture of this study, the EEG signal data has been decomposed by the Discrete Wavelet Transform (DWT) decomposition process with 4 decomposition levels and using the Daubechies family (dB). The signal decomposition results are extracted using several statistical features, which are then used for machine learning model features. The Xtreme Gradient Boost (XGBoost) algorithm was used to perform the classification task. The XGBoost model produced accuracy results of 99% (best) on individual subject tests and 98% (average) on all subjects.
KW - Brain-computer interface (BCI)
KW - Discrete Wavelet Transform (DWT)
KW - Electroencephalogram (EEG)
KW - Machine Learning
KW - Xtreme Gradient Boost (XGBoost)
UR - http://www.scopus.com/inward/record.url?scp=85163066492&partnerID=8YFLogxK
U2 - 10.1109/ICCoSITE57641.2023.10127825
DO - 10.1109/ICCoSITE57641.2023.10127825
M3 - Conference contribution
AN - SCOPUS:85163066492
T3 - ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering: Digital Transformation Strategy in Facing the VUCA and TUNA Era
SP - 354
EP - 358
BT - ICCoSITE 2023 - International Conference on Computer Science, Information Technology and Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Computer Science, Information Technology and Engineering, ICCoSITE 2023
Y2 - 16 February 2023
ER -