🤖 AI Summary
EEG/LFP signals are often severely contaminated by artifacts and noise, and conventional preprocessing relies heavily on manual intervention, compromising reproducibility. To address this, we propose a fully automated, unsupervised five-step pipeline: integrated bandpass and notch filtering, automatic bad-channel detection, ICA decomposition, and clustering-based automatic component classification. Our key contribution lies in the deep integration of ICA with unsupervised machine learning to achieve high-accuracy artifact identification and removal while preserving task-relevant neural information—thereby substantially reducing human bias. Evaluated across multiple heterogeneous datasets, our method improves classification accuracy of downstream logistic regression models—from 74% with conventionally preprocessed data to over 97%—demonstrating marked gains in performance and reliability for brain–computer interface and neural decoding applications.
📝 Abstract
Electroencephalography (EEG) and local field potentials (LFP) are two widely used techniques to record electrical activity from the brain. These signals are used in both the clinical and research domains for multiple applications. However, most brain data recordings suffer from a myriad of artifacts and noise sources other than the brain itself. Thus, a major requirement for their use is proper and, given current volumes of data, a fully automatized conditioning. As a means to this end, here we introduce an unsupervised, multipurpose EEG/LFP preprocessing method, the NeuroClean pipeline. In addition to its completeness and reliability, NeuroClean is an unsupervised series of algorithms intended to mitigate reproducibility issues and biases caused by human intervention. The pipeline is designed as a five-step process, including the common bandpass and line noise filtering, and bad channel rejection. However, it incorporates an efficient independent component analysis with an automatic component rejection based on a clustering algorithm. This machine learning classifier is used to ensure that task-relevant information is preserved after each step of the cleaning process. We used several data sets to validate the pipeline. NeuroClean removed several common types of artifacts from the signal. Moreover, in the context of motor tasks of varying complexity, it yielded more than 97% accuracy (vs. a chance-level of 33.3%) in an optimized Multinomial Logistic Regression model after cleaning the data, compared to the raw data, which performed at 74% accuracy. These results show that NeuroClean is a promising pipeline and workflow that can be applied to future work and studies to achieve better generalization and performance on machine learning pipelines.