Online Classifier of AMICA Model to Evaluate State Anxiety While Standing in Virtual Reality

G Liao, S Wang, Z Wei, 刘博涵 (Bohan Liu), R Okubo, ME Hernandez

IEEE EMBC 2022 PubMed ↗ Scholar ↗

摘要

此摘要由英文原文自動翻譯。

焦慮等情緒狀態的變化對行為與心理健康有重大影響。然而,偵測個人的焦慮需要受過訓練的專家施行專業評估,往往耗費大量時間與資源。因此,臨床實務亟需客觀且即時的焦慮偵測方法。Adaptive Mixture Independent Component Analysis(AMICA)的最新進展已展現利用腦電(EEG)資料偵測情緒狀態變化的能力。然而,由於識別不同模型可能需費時數小時,未來的腦機介面應用必須尋求替代方法。本研究探討機器學習分類器的可行性:利用腦電資料的頻域特徵,將500毫秒的腦電樣本分類至多模型AMICA標籤所確立的不同皮質狀態。使用12個腦電輸入特徵預測皮質狀態的隨機森林分類器,在二元分類中達到75%的準確率。基於這些發現,本研究可為即時焦慮狀態偵測與分類奠定基礎。

原始摘要(英文)

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 needed 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 percent accuracy in binary classification. Based on these findings, this work may provide a foundation for real-time anxiety state detection and classification.

本文貢獻

An online classifier built on AMICA EEG features that detects state anxiety while a person stands in virtual reality: real-time monitoring with applications in fall-prevention and VR therapy.

BibTeX

@inproceedings{liao2022amica,
  title     = {Online Classifier of AMICA Model to Evaluate State Anxiety While Standing in Virtual Reality},
  author    = {G Liao and S Wang and Z Wei and Bohan Liu and R Okubo and ME Hernandez},
  booktitle = {IEEE Engineering in Medicine and Biology Conference (EMBC)},
  year      = {2022}
}