Ensembling EEG bands for Mental State Assessment

Wei Ting Yen, Yun Jie Zhang, Kuo Hsuan Chung, Linen Lin, Yue Shan Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The assessment of mental status is an important task in psychiatry. But the impact of the COVID-19 epidemic has reduced the number of face-to-face assessments with physicians, and thus making it difficult. In recent years, some studies have used EEG (electroencephalogram) to help assess depression or mental state. Users can thus further assess mental state through simple EEG measurement. Since the EEG measurement will obtain multiple frequency bands related to mental or emotion state, if only one frequency band is used to evaluate a specific emotion or mental state, it may be insufficient. Some studies have proposed an ensemble method of multiple frequency bands for emotion recognition. In this study, we will use ensemble multi-bands EEG frequency to do and assist mental state or depression assessment. Through the method of ensemble learning, we integrate the frequency bands which is mainly related to mental state to assist the evaluation of mental state. From the experimental results, we can find that this method has a good effect.

Original languageEnglish
Title of host publicationICSSE 2022 - 2022 International Conference on System Science and Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages100-103
Number of pages4
ISBN (Electronic)9781665488525
DOIs
Publication statusPublished - 2022
Event2022 International Conference on System Science and Engineering, ICSSE 2022 - Virtual, Online, Taiwan
Duration: May 26 2022May 29 2022

Publication series

NameICSSE 2022 - 2022 International Conference on System Science and Engineering

Conference

Conference2022 International Conference on System Science and Engineering, ICSSE 2022
Country/TerritoryTaiwan
CityVirtual, Online
Period5/26/225/29/22

Keywords

  • EEG signal
  • Ensemble Learning
  • mental state assessment

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems

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