IJCAI 2026 Tutorial:
Brain Information Computing and Decoding for Advanced BCIs: From Basic to Frontiers

Recent advances in artificial intelligence are reshaping the field of brain-computer interfaces. This tutorial delves into two key frontiers driving this transformation.

The first part focuses on Foundation Models for Brain Signals. Traditional deep learning models are often task-specific and dataset-limited, hindering their generalization. This section will introduce a paradigm shift towards developing large-scale, self-supervised pre-trained foundation models for both non-invasive EEG signals and invasive sEEG signals. We will explore how to design spatiotemporal pre-training strategies to learn universal representations from heterogeneous multi-dataset sources, and the effective learning of time-space-frequency features. The session will cover state-of-the-art model architectures, pre-training objectives, and their subsequent fine-tuning for diverse downstream tasks.

The second part of the tutorial focuses on Invasive Brain Neural Decoding for BCI. It will provide a foundational understanding of motor BCIs, from their neural basis to the computational models. We will then delve into state-of-the-art decoding methodologies, starting from foundational state-space models and progressing to modern architectures like Mamba. We will further explore how dynamic or domain adaptation techniques can achieve stable, cross-day decoding performance, addressing one of the most critical barriers to the real-world deployment of invasive BCIs.