检查WAY EEG GAL数据集的唯一性和持久性以进行用户身份验证
Examining Uniqueness and Permanence of the WAY EEG GAL dataset toward User Authentication
摘要：
本研究评估了WAY EEG GAL公共数据集的EEG数据的鉴别能力（唯一性），以验证个体之间的相互关系及其持久性。除了EEG数据外，Luciw等人还为工程师和研究人员提供EMG（肌电图）和运动学数据，以利用WAY EEG GAL进行进一步研究。然而，评估EMG和运动学数据不在本研究范围内。最新技术的目标是确定EEG数据是否可用于控制假体装置。另一方面，本研究的目的是评估的可分性的个人通过EEG数据进行用户认证。利用特征重要性算法为每个用户选择最佳特征，以针对所有其他用户对它们进行认证。为这项研究实现的认证平台是基于机器学习模型/分类器。作为初始测试，使用线性判别分析（LDA）和支持向量机（SVM）进行两个试点研究，通过多标记EEG数据集来观察模型的学习趋势。首先使用kNN作为用户认证的分类器，观察到约75%的准确率。此后，为了提高性能，线性和非线性SVM都被用来执行分类。使用线性和非线性支持向量机分别实现了85.18%和86.92%的总体平均准确率。除了准确性，还计算F1分数。线性和非线性支持向量机的总体平均F1得分分别为87.51%和88.94%。除了整体性能之外，还观察到使用线性SVM具有95.3%准确度（95.3%F1得分）和使用非线性SVM具有97.4%准确度（97.3%F1得分）的高性能个体。
英文摘要：
This study evaluates the discriminating capacity (uniqueness) of the EEG data from the WAY EEG GAL public dataset to authenticate individuals against one another as well as its permanence. In addition to the EEG data, Luciw et al. provide EMG (Electromyography), and kinematics data for engineers and researchers to utilize WAY EEG GAL for further studies. However, evaluating the EMG and kinematics data is outside the scope of this study. The goal of the state-of-the-art is to determine whether EEG data can be utilized to control prosthetic devices. On the other hand, this study aims to evaluate the separability of individuals through EEG data to perform user authentication. A feature importance algorithm is utilized to select the best features for each user to authenticate them against all others. The authentication platform implemented for this study is based on Machine Learning models/classifiers. As an initial test, two pilot studies are performed using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) to observe the learning trends of the models by multi-labeling the EEG dataset. Utilizing kNN first as the classifier for user authentication, accuracy around 75% is observed. Thereafter to improve the performance both linear and non-linear SVMs are used to perform classification. The overall average accuracies of 85.18% and 86.92% are achieved using linear and non-linear SVMs respectively. In addition to accuracy, F1 scores are also calculated. The overall average F1 score of 87.51% and 88.94% are achieved for linear and non-linear SVMs respectively. Beyond the overall performance, high performing individuals with 95.3% accuracy (95.3% F1 score) using linear SVM and 97.4% accuracy (97.3% F1 score) using non-linear SVM are also observed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
