Deep learning in auditory attention decoding: A systematic review

Auditory attention decoding (AAD) utilizes electroencephalography (EEG) to detect an individual's attentional focus in noisy, multi-speaker environments, serving as a cornerstone for next-generation neuro-steered hearing aids. Although deep learning has recently surpassed traditional linear models in decoding accuracy, a holistic overview connecting preprocessing pipelines with advanced model architectures is lacking. This systematic review addresses this deficiency by analyzing 82 studies selected via PRISMA guidelines. We comprehensively dissect the AAD framework, from data processing to the evolution of architectures like convolutional neural networks (CNNs), graph neural networks (GNNs), and Transformers. Beyond reviewing trends, we synthesize practical design guidance, recommending the alignment of preprocessing complexity with model capacity and the use of hybrid architectures to capture spatiotemporal dynamics. Crucially, this study highlights persistent challenges impeding real-world transferability, including the reliance on high-density EEG montages incompatible with wearables, the computational latency on edge devices, and the lack of realistic acoustic scenarios. By identifying these bottlenecks and synthesizing effective design choices, this review offers a structured reference for developing robust, low-latency, and subject-independent auditory attention detection systems suitable for practical brain-computer interface applications.