Enhancing EEG-Based User Verification with a Normalized Neural Network Ensemble Approach

The development of user identity verification approaches using biometric systems based on EEG data holds significant promise across various domains. However, the inherent complexity and variability of this data make designing reliable solutions challenging. In response to these challenges, this work introduces a Normalized Neural Network Ensemble (NNNE) approach for EEG-based user verification. It leverages neural networks to enhance the current state-of-the-art performance, aiming to overcome the problems associated with EEG data by capturing spatial and temporal patterns in EEG signals more effectively. In detail, the proposed approach relies on an architecture centered around an ensemble of Multi-Layer Perceptron artificial neural networks regulated by a soft voting criterion. As part of the preprocessing steps, the input data is normalized by transforming features based on quantile information. Additionally, the MLP hyperparameters and the number of MLP evaluators in the ensemble are automatically optimized. Considering the high heterogeneity of the state-of-the-art works in this field, which are characterized by a wide variability in the choices of components, approaches, and strategies, making comparisons between their performances difficult and sometimes impossible, this paper exploits the opportunity offered by the Biometric EEG Dataset (BED), which provides benchmark values that facilitate comparisons within the context of widely adopted approaches in literature in terms of stimuli and feature extraction techniques. The experimental results show that the proposed NNNE approach improves the performance of the state-of-the-art one (Hidden Markov Model) used by the authors of the dataset to define the reference values, significantly.