[2506.09110] CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model

Computer Science > Machine Learning arXiv:2506.09110 (cs) [Submitted on 10 Jun 2025 (v1), last revised 29 Apr 2026 (this version, v3)] Title:CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model Authors:Jingying Ma, Feng Wu, Qika Lin, Yucheng Xing, Chenyu Liu, Ziyu Jia, Mengling Feng View a PDF of the paper titled CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model, by Jingying Ma and 6 other authors View PDF HTML (experimental) Abstract:Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models, current approaches still yield clinically uninterpretable and weakly discriminative representations, inefficiently capturing global dependencies and neglecting important local neural events. We present CodeBrain, a two-stage EFM designed to fill this gap. In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens, quadratically expanding the representation space to enhance discriminative power and offering domain-specific representation-level interpretability by suggesting potential links to neural events and spectral rhythms. In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with slid...