EEG-based Person Identification System and Its Longitudinal Adaptation
本研究針對腦電波身分辨識系統提出了兩階段式身分辨識與長時調變機制，來分別改善準確率與穩定度。前者可在維持正確接受率的情況下，進一步降低錯誤接受率；後者則可因應腦電波特徵隨時間之變化來調變系統。兩階段式系統中包含了分類部分以及驗證部分。腦電波訊號經過自身回歸模型（autoregressive model, AR）與頻帶特徵的運算後，再透過多分類組別之支持向量機（support vector machine, SVM）來進行分類。而驗證部分是由候補者篩選(candidate selection)、資料重新表述(re-representation)和身分驗證三步驟所構成。我們首先運用線性區分分析（linear discriminant analysis, LDA）進行資料的重新表述，再由兩分類群組之支持向量機與最近相鄰分類器驗證分類結果。
另一方面，長時調變機制則分為漸進式學習（incremental learning）與在分類前先行調整資料分佈的適應性系統(adaptive system)。在適應性系統裡，我們探討了主成分分析與領域調整（domain adapatation）兩種方法。該系統是根據新資料來調整訓練資料的分佈，再利用調整過的內容重建分類器並進行身分辨識。
Biometrics has been viewed as an alternative to conventional person identification methods due to its universality, portability, and resistance in duplication. Previous study indicated that electroencephalography (EEG) carries discriminative information for distinguishing individuals and has great potential to meet the requirements of high security level. Therefore, it is essential to improve the reliability and stability of the EEG-based biometrics system for the promotion of its applicability. In this study, we first developed a two-stage EEG-based person identification system to improve the reliability by lowering the false acceptance rate while maintaining the true acceptance rate. The second purpose was to improve the stability of the EEG-based identification system using longitudinal adaptation. In addition to the classification stage, the proposed two-stage identification system re-examines the classification results in another verification stage, which consisted of candidate selection, re-representation, and verification steps. The classifier applied in the classification step was a multi-class support vector machine (SVM), while a binary SVM or kNN classifier was utilized in the verification step. Additionally, the linear discriminant analysis (LDA) was utilized to re-represent the training data before the verification step. On the other hand, the longitudinal adaptation was accomplished by either incremental learning or an adaptive system. In the adaptive system, we applied the principal component analysis (PCA) or domain adaptation (DA) to transform the data distribution toward the newly acquired data, and then we construct a new classifier with the adapted data. For each of the 23 participants, 90 EOG-free trials of the EEG recordings were acquired during the lifting of left index finger. The experimental results showed that our two-stage identification process reduced the false acceptance rate from 9.5% to 5.4% while maintaining the true acceptance rate (form 90.5% to 87.7%). The evaluation results showed that the power spectral density (PSD) was the most stable feature, and the PCA-based adaptive system can improve the true acceptance rate by 5.2% when using the PSD features. Furthermore, the identification system using incremental learning can even improve the true acceptance rate from 12.2% to 58.9%.
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