# How EEG preprocessing shapes decoding performance

arXiv: https://arxiv.org/abs/2410.14453 · HTML: https://mlsanity.com/paper/2410.14453/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2024.

## Built on by (most cited first, 7 of 7)

- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability](https://mlsanity.com/paper/2605.07212/) (2026)
- [Data Normalization Strategies for EEG Deep Learning](https://mlsanity.com/paper/2506.22455/) (2025)
- [Leakage-Audited Benchmarking Reveals Limited Evidence for Cross-Subject Auditory-Evoked EEG Vowel Perception Decoding](https://mlsanity.com/paper/2605.00865/) (2026)
- [L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark](https://mlsanity.com/paper/2605.22893/) (2026)
- [Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components](https://mlsanity.com/paper/2605.24558/) (2026)
- [NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication](https://mlsanity.com/paper/2606.20673/) (2026)

## Abstract (arXiv)

EEG preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE dataset. We systematically varied key preprocessing steps, such as filtering, referencing, baseline interval, detrending, and multiple artifact correction steps. Then we performed trial-wise binary classification (i.e., decoding) using neural networks (EEGNet), or time-resolved logistic regressions. Our findings demonstrate that preprocessing choices influenced decoding performance considerably. All artifact correction steps reduced decoding performance across experiments and models, while higher high-pass filter cutoffs consistently increased decoding performance. For EEGNet, baseline correction further increased decoding performance, and for time-resolved classifiers, linear detrending, and lower low-pass filter cutoffs increased decoding performance. The influence of other preprocessing choices was specific for each experiment or event-related potential component. The current results underline the importance of carefully selecting preprocessing steps for EEG-based decoding. While uncorrected artifacts may increase decoding performance, this comes at the expense of interpretability and model validity, as the model may exploit structured noise rather than the neural signal.