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

TL;DR

CLEAN-MI is a comprehensive pipeline that enhances the quality and standardization of large-scale EEG datasets for motor imagery BCIs, improving model training and classification accuracy.

Contribution

The paper introduces CLEAN-MI, a novel scalable pipeline combining filtering, selection, and alignment techniques to improve neurodata quality in MI-based BCIs.

Findings

Improved classification performance on multiple datasets.

Enhanced data quality and consistency across sources.

Systematic filtering reduces noise and heterogeneity.

Abstract

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.