Impact of Data Quality on EEG-Based Biomedical Systems

With the growing use of biomedical signal acquisition systems such as EEG, ensuring high-quality data acquisition in resource-constrained environments remains a critical challenge. This study evaluates how data quality-specifically noise, error, and loss-affects the performance of a CNN-based epileptic seizure detection system in terms of accuracy, execution time, and power consumption. We simulate different corruption scenarios and propose strategies for tolerance assessment and lightweight compensation. The results provide insights for building adaptive EEG-based biomedical systems.