Design and Implementation of a Customizable EEG Dataset Construction System

Electroencephalography (EEG) plays a pivotal role in diagnosing neurological conditions, such as epilepsy and cognitive impairments. The efficacy of EEG analysis heavily relies on the quality and diversity of the underlying dataset. A significant challenge in EEG data analysis is the pronounced class imbalance, where certain neurological patterns are underrepresented, potentially skewing predictive models and diagnostic accuracy. Moreover, the absence of negative examples in EEG datasets can lead to misclassifications and limit the generalizability of findings. To address these issues, we developed a sophisticated, modular EEG dataset construction system designed to enhance the integration of both positive and negative examples, thereby mitigating class imbalance. This system is highly flexible and scalable, allowing for the tailored creation of datasets that meet specific research and clinical demands. This paper details the design, implementation, and validation of the system, demonstrating its capability to construct balanced datasets that are essential for robust EEG analysis. The results affirm the system's efficacy in fostering more accurate and comprehensive diagnostic and research outcomes in the field of neurology.