TY - GEN
T1 - Online classifier of AMICA model to evaluate state anxiety while standing in virtual reality
AU - Liao, Gekai
AU - Wang, Siwen
AU - Wei, Zijing
AU - Liu, Bohan
AU - Okubo, Ryu
AU - Hernandez, Manuel E.
N1 - *This work was supported by a JUMP ARCHES Grant 1G. Liao is with the Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. gekail2 at illinois.edu 2S. Wang is with the Department of Bioengineering, UC San Diego, La Jolla, CA, USA. siw028 at ucsd.edu 3Z. Wei is with the Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA. zijingw4 at illinois.edu 4B. Liu is with the Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana,IL, USA. bohan3 at illinois.edu 5R. Okubo is with the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana,IL, USA. rokubo2 at illinois.edu 6M.E. Hernandez is with the Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, USA. mhernand at illinois.edu
PY - 2022
Y1 - 2022
N2 - Changes in emotional state, such as anxiety, have a significant impact on behavior and mental health. However, the detection of anxiety in individuals requires trained specialists to administer specialized assessments, which often take a significant amount of time and resources. Thus, there is a significant need for objective and real-time anxiety detection methods to aid clinical practice. Recent advances in Adaptive Mixture Independent Component Analysis (AMICA) have demonstrated the ability to detect changes in emotional states using electroencephalographic (EEG) data. However, given that several hours may be need to identify the different models, alternative methods must be sought for future brain-computer-interface applications. This study examines the feasibility of a machine learning classifier using frequency domain features of EEG data to classify individual 500 ms samples of EEG data into different cortical states, as established by multi-model AMICA labels. Using a random forest classifier with 12 input features from EEG data to predict cortical states yielded a 75% accuracy in binary classification. Based on these findings, this work may provide a foundation for real-time anxiety state detection and classification.
AB - Changes in emotional state, such as anxiety, have a significant impact on behavior and mental health. However, the detection of anxiety in individuals requires trained specialists to administer specialized assessments, which often take a significant amount of time and resources. Thus, there is a significant need for objective and real-time anxiety detection methods to aid clinical practice. Recent advances in Adaptive Mixture Independent Component Analysis (AMICA) have demonstrated the ability to detect changes in emotional states using electroencephalographic (EEG) data. However, given that several hours may be need to identify the different models, alternative methods must be sought for future brain-computer-interface applications. This study examines the feasibility of a machine learning classifier using frequency domain features of EEG data to classify individual 500 ms samples of EEG data into different cortical states, as established by multi-model AMICA labels. Using a random forest classifier with 12 input features from EEG data to predict cortical states yielded a 75% accuracy in binary classification. Based on these findings, this work may provide a foundation for real-time anxiety state detection and classification.
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U2 - 10.1109/EMBC48229.2022.9871843
DO - 10.1109/EMBC48229.2022.9871843
M3 - Conference contribution
C2 - 36086599
AN - SCOPUS:85138127630
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 381
EP - 384
BT - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Y2 - 11 July 2022 through 15 July 2022
ER -