---
id: 20260317-T0-21
title: "arXiv论文提出脑机接口合成数据生成基准与评估方法"
title_en: "arXiv Paper Proposes Synthetic Data Benchmark for Brain-Computer Interfaces"
url: https://ai.daily.yangsir.net/daily/20260317-T0-21
issue_date: 2026-03-17
publish_date: 2026-03-16T04:00:00.000Z
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2603.12296
---

# arXiv论文提出脑机接口合成数据生成基准与评估方法

arXiv论文2603.12296概述了脑机接口（BCI）的合成数据生成方法、基准测试和未来方向。研究指出，深度学习的进步依赖大规模高质量数据，而BCI发展受限于有限数据。论文提出了一套合成数据生成框架，涵盖EEG、fMRI等模态，并通过与真实数据对比验证了其有效性。该框架可提升BCI模型在运动想象、情感识别等任务上的性能，为研究者提供了可扩展的数据解决方案。

## English Version

**arXiv Paper Proposes Synthetic Data Benchmark for Brain-Computer Interfaces**

arXiv paper 2603.12296 presents an overview of synthetic data generation, benchmarking, and future directions for brain-computer interfaces (BCIs). The research addresses BCIs' data limitation by proposing a framework for generating synthetic EEG and fMRI data. Experiments show the synthetic data improves performance in motor imagery and emotion recognition tasks. The work provides researchers with a scalable solution to train more robust BCI models.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2603.12296)

**详情页**：https://ai.daily.yangsir.net/daily/20260317-T0-21

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