Decoding Pathway Selection for Resource-Constrained Brain-Computer Interfaces: A Systematic Trade-off between Conventional Feature Engineering and Lightweight Deep Learning

Abstract

The evolution of Brain-Computer Interfaces (BCI) towards portable, wearable, and edge computing scenarios presents stringent challenges for decoding algorithms, demanding real-time processing under constraints of limited computational power, memory, and energy consumption. This paper provides a systematic review and comparative analysis of two core technical pathways to address this challenge: conventional feature engineering based on explicit signal processing and data-driven lightweight deep learning. Conventional methods (e.g., Common Spatial Pattern (CSP), Canonical Correlation Analysis (CCA)) rely on expert knowledge, maintaining dominance in extremely resource-constrained scenarios due to their superior computational efficiency, low power consumption, and high interpretability. Lightweight deep learning methods (e.g., EEGNet, a compact convolutional neural network designed for electroencephalogram (EEG) decoding), through end-to-end learning, demonstrate advantages in decoding accuracy, especially for handling complex tasks, though their model compression and deployment processes are more complex. This paper constructs a multi-dimensional trade-off analysis framework, conducting an in-depth comparison of the two pathways from aspects such as accuracy, computational efficiency, energy consumption, data requirements, interpretability, and deployment complexity. The analysis indicates that the selection of a solution highly depends on the priority constraints of the target application: scenarios with extreme demands for power consumption and determinism (e.g., implantable devices) lean towards traditional methods; whereas for complex tasks (e.g., cognitive state monitoring) that require higher accuracy and have relatively sufficient resources, lightweight deep learning is a better choice. Finally, this paper focuses on the future. He talked about such things as edge adaptation of EEG basic model, hybrid architecture of brain insertion and collaborative design of hardware and software. The purpose is to provide a clear framework for selecting decoders in different BCI application scenarios on the edge.