This study aims to identify the most efficient and cost-effective approach to addressing the computational challenges associated with EEG-based biometric systems that use machine learning models. Traditional brain signal biometric analysis methods often extract features from a single EEG channel or a randomly selected subset of channels from the same brain region. However, given that essential physiological information is functionally distributed across multiple brain regions, this study systematically explores various combinations of EEG channels both within and across different brain regions. The biometric distinctiveness of EEG signals is evaluated under different emotional simulation conditions. Addressing the channel selection problem is critical, as it can lead to improved identification of optimal EEG sensor locations. EEG data were collected using all 32 available channels and the experimental results revealed that an optimally selected subset of eight channels outperformed the complete set of 32 channels in classification performance.