Abstract
Episodic memory training plays a crucial role in cognitive enhancement, particularly in addressing age-related memory decline and cognitive disorders. Accurately assessing the effectiveness of such training requires reliable methods to capture changes in memory function. Electroencephalography (EEG) offers an objective way of evaluating neural activity before and after training. However, EEG classification in episodic memory assessment remains challenging due to the variability in brain responses, individual differences, and the complex temporal-spatial dynamics of neural signals. Traditional EEG classification methods, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), face limitations when applied to episodic memory training assessment, struggling to extract meaningful features and handle the inherent uncertainty in EEG signals. To address these issues, this paper introduces DU-former, which improves feature extraction and enhances the model's robustness against noise. Specifically, data uncertainty (DU) explicitly handles data uncertainty by modeling input features as Gaussian distributions within the reparameterization module. One branch predicts the mean through convolution and normalization, while the other estimates the variance via average pooling and normalization. These values are then used for Gaussian reparameterization, enabling the model to learn more robust feature representations. This approach allows the model to remain stable when dealing with complex or noisy data. To validate the method, an episodic memory training experiment was designed with 17 participants who underwent 28 days of training. Behavioral data showed a significant reduction in task completion time. Object recognition accuracy also improved, as indicated by the higher proportion of correctly identified target items in the episodic memory testing game. Furthermore, EEG data collected before and after the training were used to evaluate the DU-former's performance, demonstrating significant improvements in classification accuracy. This paper contributes by introducing uncertainty learning and proposing a more efficient and robust method for EEG signal classification, demonstrating superior performance in episodic memory assessment.