This Thesis delves into the intersection of Deep Learning and Motor-Imagery (MI) Brain-Computer Interfaces (BCIs), aiming to enhance their effectiveness and applicability through innovative approaches and frameworks.

In the first part of the Thesis, we present an extensive EEG data collection experiment, spanning two years and encompassing diverse scenarios. By meticulously designing the experimental protocols and utilizing cutting-edge EEG hardware, we captured and analyzed real and imagined movements of hundreds of participants. The first part of this Thesis also introduces a novel causal framework integrating Causal Reasoning into brainwave modeling for all BCI paradigms. Using this framework, we analyse the challenges of brainwave decoding in real-world BCI applications, exploring ways to address these challenges using general machine learning practices. This framework is the first study to combine Machine Learning and Causal Reasoning in the field of BCIs and to present a unified causal framework. Although theoretical in nature, we posit that this causal framework holds promise for future BCI enhancements and aids in pinpointing potential pitfalls within machine learning systems endeavoring to solve the complex problem of exploiting the brain.

In the second part of this Thesis, we introduce a lightweight deep neural network architecture leveraging the joint time-frequency scattering transform, resulting in improved classification performance and enhanced interpretability. Additionally, a novel subject selection framework is presented, enhancing personalised performance for subjects, especially those who initially exhibited poor performance. Finally, the issue of inter-subject variability in MI decoding, which hinders the generalization of deep models across subjects, is addressed by introducing a dynamic convolution framework, demonstrating improved generalization performance across subjects in various MI tasks.

Collectively, this Thesis presents a comprehensive exploration of key challenges in BCI research and proposes innovative solutions, both theoretical and practical, to advance the field, ultimately paving the way for more effective and robust BCIs.