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The editorial aims to highlight the role of deep learning in advancing brain-computer interfaces across multiple applications.

Editorial: Deep learning in brain-computer interfaces

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Editorial discusses advancements in brain-computer interfaces using deep learning for communication and emotional recognition.

The editorial aims to highlight the role of deep learning in advancing brain-computer interfaces across multiple applications.
Review of four articles on motor imagery decoding, emotion recognition, and inner speech decoding.
Integration of traditional signal processing techniques with deep learning models.
Discussion of challenges such as signal variability and the need for improved model interpretability.
Successful application of MCFANet for efficient motor imagery EEG classification incorporating spatial and temporal features.
Introduction of Multilayer-GTCN for emotion recognition to enhance understanding of emotional states from EEG data.
Highlighting the potential for inner speech decoding and ethical concerns surrounding the use of BCI technology.

Solé-Casals et al. (2026) studied this question.

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