Khalil Adnan Elahi Khan, Perez-Diaz Jesus Arturo, Cantoral-Ceballos Jose Antonio, Antelis Javier M
School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Nuevo Leon, Mexico.
Sensors (Basel). 2024 Dec 11;24(24):7919. doi: 10.3390/s24247919.
With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest within the scientific community over the past decade. Most previous efforts have focused on identifying distinctive information within electroencephalogram (EEG) recordings. In this study, an EEG-based user authentication scheme is presented, employing a multi-layer perceptron feedforward neural network (MLP FFNN). The scheme utilizes P300 potentials derived from EEG signals, focusing on the user's intent to select specific characters. This approach involves two phases: user identification and user authentication. Both phases utilize EEG recordings of brain signals, data preprocessing, a database to store and manage these recordings for efficient retrieval and organization, and feature extraction using mutual information (MI) from selected EEG data segments, specifically targeting power spectral density (PSD) across five frequency bands. The user identification phase employs multi-class classifiers to predict the identity of a user from a set of enrolled users. The user authentication phase associates the predicted user identities with user labels using probability assessments, verifying the claimed identity as either genuine or an impostor. This scheme combines EEG data segments with user mapping, confidence calculations, and claimed user verification for robust authentication. It also accommodates new users by transforming EEG data into feature vectors without the need for retraining. The model extracts selected features to identify users and to classify the input based on these features to authenticate the user. The experiments show that the proposed scheme can achieve 97% accuracy in EEG-based user identification and authentication.
随着人工智能最近取得的重大进展，对更可靠的识别系统的需求迅速增加，以保护个人资产。在过去十年中，使用脑信号进行身份验证在科学界引起了广泛关注。以前的大多数努力都集中在识别脑电图（EEG）记录中的独特信息。在本研究中，提出了一种基于EEG的用户身份验证方案，采用多层感知器前馈神经网络（MLP FFNN）。该方案利用从EEG信号中提取的P300电位，重点关注用户选择特定字符的意图。这种方法包括两个阶段：用户识别和用户身份验证。两个阶段都利用脑信号的EEG记录、数据预处理、用于存储和管理这些记录以便高效检索和组织的数据库，以及使用互信息（MI）从选定的EEG数据段中提取特征，特别是针对五个频段的功率谱密度（PSD）。用户识别阶段使用多类分类器从一组注册用户中预测用户的身份。用户身份验证阶段使用概率评估将预测的用户身份与用户标签相关联，验证所声称的身份是真实的还是冒名顶替者。该方案将EEG数据段与用户映射、置信度计算和所声称的用户验证相结合，以进行可靠的身份验证。它还通过将EEG数据转换为特征向量来容纳新用户，而无需重新训练。该模型提取选定的特征以识别用户，并根据这些特征对输入进行分类以验证用户身份。实验表明，所提出的方案在基于EEG的用户识别和身份验证中可以达到97%的准确率。
