STEAM:ASpatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding
Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios. However, conventional neural signal decoding algorithms often suffer from limited generalizability and high adaptation costs, motivating recent interest in BCI foundation models. Existing approaches still struggle to jointly achieve general transferability, accurate decoding, and efficient downstream adaptation. We present STEAM, a hierarchical transfer frame
Record details
Published: 3 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 4 August 2026
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ethics.ai (3 August 2026), “STEAM:ASpatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding,” evidence record 16149, https://ethics.ai/record/16149 (originally published by arXiv cs.LG).
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