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The domain of brain-computer interfaces (BCIs) represents a field at the confluence of neuroscience, artificial intelligence, and human-machine interaction. BCIs have been heralded as transformative tools capable of restoring, augmenting, or bypassing neural and motor functions, fundamentally reshaping how humans interact with technology. This field has recently experienced explosive growth, spurred by the integration of deep learning techniques—a revolutionary array of methods that are reshaping our understanding and interpretation of complex neural data. Notable advances in neural signal processing, brought about by deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) have substantially enhanced the efficacy of BCIs. These architectures, with their ability to decode intricate neural activity patterns, have successfully harnessed a range of recording modalities—from electroencephalography (EEG) to functional magnetic resonance imaging (fMRI)—and have elevated our capacity to achieve high levels of accuracy in fundamental BCI tasks.

This Research Topic aims to explore the diverse applications of deep learning within BCIs, seeking submissions that include original research or innovative methodologies, as well as comprehensive reviews that address the complete gamut of BCI challenges—from signal acquisition to neural decoding and effector control. A critical focus will be on initiatives that merge algorithmic innovation with real-world application.

To gather further insights into the field of deep learning in Brain-Computer Interfaces, we welcome articles addressing, but not limited to, the following themes:

Novel deep learning architectures optimized for neural signal analysis and decoding

End-to-end learning from raw brain data with minimal pre-processing

Transfer learning and domain adaptation methods for BCIs across individuals and contexts

Hybrid models combining deep learning with traditional signal processing or machine learning approaches

Interpretable and explainable deep neural networks for BCI transparency

Robustness and generalizability of BCI systems in real-world environments

Real-time deep learning-based closed-loop BCIs

Deep generative models for neural activity simulation and decoding

Comparison of deep learning based decoding with conventional BCI decoding algorithms.

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Deep learning in brain-computer interfaces