| name | cambrain-realtime-eeg-inference |
| description | CaMBRAIN methodology for real-time continuous EEG inference using causal Mamba state space models. First model enabling long-range streaming inference of variable-length EEG signals with >10x higher throughput. |
| version | 1.0.0 |
| author | arXiv paper extraction |
| arxiv_id | 2605.28792 |
| activation_keywords | ["EEG","real-time inference","state space model","Mamba","causal model","continuous EEG","streaming inference","brain activity monitoring","neural signal processing"] |
| tags | ["neuroscience","EEG","state-space-models","causal-inference","real-time-processing","deep-learning","computational-neuroscience"] |
| related_skills | ["jet-eeg-flow-matching","eeg-foundation-model-adapters","mamba-spike-forecaster-bci","eeg-ieeg-bridge-bci"] |
CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models
arXiv ID: 2605.28792
Authors: Abhilash Durgam, Nyle Siddiqui, Jeffrey A. Chan-Santiago, Qiushi Fu, Elakkat D. Gireesh, Mubarak Shah
Submission Date: 2026-05-27
Categories: cs.AI, cs.HC, cs.LG
Overview
CaMBRAIN is the first Causal, Mamba-based state space model (SSM) capable of real-time inference of EEG signals. It addresses critical limitations in existing EEG deep learning approaches:
- Quadratic scaling problem: Traditional attention-based models scale quadratically with sequence length
- Fixed-length input constraint: Raw EEG must be processed in sliding windows, preventing global signal understanding
Core Innovation
Key Arguments
- Bidirectional approaches are needlessly expensive for EEG processing
- EEG is inherently causal and unidirectional - past signals influence future, not vice versa
- Causal SSM architecture is more appropriate than bidirectional attention
Technical Challenge
EEG exhibits extreme temporal dynamics:
- Crucial events can be extremely brief (fractions of a second)
- Events separated by long intervals (minutes)
- Current self-supervised objectives optimize for signal reconstruction
- These objectives fail to train hidden state to retain salient long-range context
Methodology
Multi-Stage Self-Supervised Training Pipeline
Designed specifically for streaming SSMs to:
- Encourage long-range memory retention
- Preserve linear-time complexity of state space models
- Explicitly train hidden state for streaming inference context
Architecture Components
- Causal Mamba backbone: Unidirectional processing aligned with EEG temporal structure
- Streaming-friendly design: Continuous inference without sliding-window limitations
- Linear-time complexity: O(n) scaling vs O(n²) attention
Performance Results
State-of-the-art across 3 different EEG datasets:
- >10x higher throughput than existing models
