# CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm

arXiv: https://arxiv.org/abs/2506.11830 · HTML: https://mlsanity.com/paper/2506.11830/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification](https://mlsanity.com/paper/2507.20254/) (2025)
- [The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results](https://mlsanity.com/paper/2509.10463/) (2025)
- [FusionGen: Feature Fusion-Based Few-Shot EEG Data Generation](https://mlsanity.com/paper/2510.10604/) (2025)

## Abstract (arXiv)

The construction of large-scale, high-quality datasets is a fundamental prerequisite for developing robust and generalizable foundation models in motor imagery (MI)-based brain-computer interfaces (BCIs). However, EEG signals collected from different subjects and devices are often plagued by low signal-to-noise ratio, heterogeneity in electrode configurations, and substantial inter-subject variability, posing significant challenges for effective model training. In this paper, we propose CLEAN-MI, a scalable and systematic data construction pipeline for constructing large-scale, efficient, and accurate neurodata in the MI paradigm. CLEAN-MI integrates frequency band filtering, channel template selection, subject screening, and marginal distribution alignment to systematically filter out irrelevant or low-quality data and standardize multi-source EEG datasets. We demonstrate the effectiveness of CLEAN-MI on multiple public MI datasets, achieving consistent improvements in data quality and classification performance.