The Evolution of Neural Signal Decoding Techniques in Brain-Computer Interfaces from Traditional Methods to Deep Learning

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices by decoding neural signals, effectively bypassing traditional peripheral pathways. Traditional BCI systems primarily relied on machine learning pipelines involving preprocessing, feature extraction, and classification. However, these approaches often faced challenges in generalizing across subjects and sessions. In recent years, the integration of deep learning has greatly enhanced neural signal decoding, enabling end-to-end modeling and improving adaptability. This paper explores the evolution of decoding techniques from traditional methods to state-of-the-art deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs) like long short-term memory networks (LSTMs), Transformer architectures, and self-supervised learning frameworks. Moreover, it discusses critical challenges such as low signal quality, high individual variability, limited data availability, and real-time processing constraints. The results suggest that deep learning methods in BCI neural signal decoding address traditional limitations by boosting accuracy and real-time performance. However, challenges like signal quality, individual variability, and real-time processing constraints persist. Thus, future developments at the convergence of artificial intelligence and BCI technologies highlight the imperative to integrate ethical and societal considerations with ongoing technological innovation.