Autoencoder-Based Drift Detection Method for Dynamic Analysis of EEG Data: A Comprehensive Study

In distributed data environments, the properties and probabilities of the data may evolve over time creating an occurrence called Concept Drift. Indeed, ensuring the effectiveness and dependability of machine learning models is greatly dependent on concept drift detection, especially in areas where the distribution of data is affected by periodic changes. Hence, in dynamic environments, such as EEG datasets, the concept drift poses an important challenge. This study presents the autoencoder approach for identifying concept drift in EEG datasets made up of children with normal development and ADHD (Attention Deficit Hyperactivity Disorder). After conducting many tests with different window sizes, we determined the number of batches in the two datasets (Normal and ADHD). The experimental results show how well the suggested technique works to identify shifts in the distributions of EEG data, which helps to maintain precise prediction models in dynamic settings.