Fast and Lightweight CNN Model for EEG Person Identification on Constrained Hardware

Electroencephalography (EEG)-based person identification has emerged as a secure and spoof-resistant biometric solution, leveraging the uniqueness of individual brain activity. While convolutional neural networks (CNNs) have demonstrated strong performance in this domain, existing models often require large numbers of parameters and high-dimensional inputs due to reliance on many EEG channels, leading to excessive computational demands. Recent works have attempted to introduce lightweight models by reducing the number of EEG channels and network parameters. Yet, these approaches still generate excessive amounts of input data, which significantly slows down training and increases computational cost. This undermines the core objective of lightweight modeling by shifting the computational burden to the data pipeline, making the overall system far from efficient. We introduce a CNN-based EEG identification model that is lightweight in every aspect from architecture to data pipeline. The model uses only three EEG channels and 3-second input segments, keeping data compact and efficient. With approximately 64,000 learnable parameters—making it, to the best of our knowledge, the most lightweight CNN-based EEG identification model reported—it trains rapidly, requiring only ~2 seconds per epoch on a modest CPU. Despite its simplicity, the model achieves a 98.2\% rank-1 test accuracy. This balance of accuracy, speed, and ultra-low complexity makes the model especially suitable for small-scale applications, portable EEG devices, and scenarios where developers lack access to high-end GPUs or CPUs.