AutoBCI finds better brain computer interface designs using AI agents
AutoBCI: Forecast-Guided Agentic Neural Architecture Discovery for EEG-Based Brain--Computer Interfaces
Artificial Intelligence
Summary
Brain computer interfaces (BCIs) read brain signals to control computers, but designing the software for this is hard due to many tasks. The authors created AutoBCI, a system where two AI agents work together: one designs brain signal decoding methods and the other guesses how well these methods will perform early on. This helps find effective designs faster and across different brain-related tasks like recognizing emotions or sleep states. Their approach slightly outperforms existing models on various test sets.
What this means in practice
- •For bci system developers: Select and improve EEG decoding models for diverse tasks using AutoBCI’s agent-guided search to boost accuracy and efficiency.
- •For health monitoring teams: Develop better sleep staging and emotion recognition tools by adopting AutoBCI’s model discovery framework.
Authors
Muyun Jiang, Yi Ding, Wei Zhang, Jinbo Chen, Chenyu Liu, Zhenjie Yang, Yuxin Li, Jingyuan Chen, Yuhao Lu, Yong Li, Shuailei Zhang, Cuntai Guan
Abstract
EEG-based brain-computer interfaces support a broad range of applications, yet designing decoding architectures that perform well across diverse tasks remains challenging. We introduce AutoBCI, an agentic framework in which a Designer Agent and a Forecaster Agent support the discovery and selection of EEG decoding architectures across tasks. The Designer Agent performs Pool-Guided Architecture Discovery (PGAD), generating and refining architectures through training and validation across multiple EEG tasks, such as emotion recognition, motor imagery, and sleep staging. The Forecaster Agent performs Performance Estimation from Early Knowledge (PEEK), using architecture code, the training protocol, and early learning curves to predict full-budget validation performance and select promising candidates for continued training. Across 14 EEG datasets spanning motor imagery, emotion recognition, and sleep staging, we evaluate AutoBCI with six LLMs, including Opus 5.5 and GPT 5.6 Sol, and compare the architectures selected by the search procedure against ten baselines: six conventional EEG models and four foundation models. The architecture discovered by AutoBCI with Claude Opus 5.5 achieves 64.16% average test balanced accuracy (bAcc), compared with 63.87% for REVE, the strongest baseline on this metric. Using ten observed epochs, PEEK reduces mean absolute error in predicting average validation bAcc from 2.20 to 1.36 percentage points, a 38.1% reduction relative to the best-observed-score baseline.