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Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates.

Hendrik Eilts¹, Gabriel Ivucic¹, Niklas Koenen²

¹Cognitive Systems Lab, Universität Bremen, Bremen, Germany.

Human Brain Mapping

|April 27, 2026

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This summary is machine-generated.

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This study introduces a new framework, Concept Relevance Propagation (CRP), to interpret deep learning models like EEGNet used in electroencephalography (EEG) analysis. CRP reveals how these models learn neural patterns, offering insights into brain function and task-specific neuroscience.

Area of Science:

Neuroscience
Machine Learning
Signal Processing

Background:

Deep learning significantly enhances electroencephalography (EEG) analysis but lacks interpretability.
Understanding what deep learning models learn from neural data is crucial for neuroscientific insights.

Purpose of the Study:

To develop a comprehensive interpretability framework for deep learning models of neural data.
To analyze abstract concepts encoded by neurons and filters in EEG models.
To bridge the gap between deep learning features and neuroscientific concepts.

Main Methods:

Concept Relevance Propagation (CRP), an extension of layer-wise relevance propagation, was developed.
CRP was applied to filters of EEGNet models trained via leave-one-out cross-validation.

Keywords:

BCI CRP EEG XAI

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Relevance maximization, UMAP dimensionality reduction, and density-based clustering identified filter concepts.

A virtual inspection layer projected explanations into the frequency domain for spatial, temporal, and spectral analysis.

Main Results:

The framework revealed interpretable, task-relevant neural patterns across auditory attention, attention, and motor imagery tasks.
Learned features generalized across participants, demonstrating robustness.
The approach provided insights into task-specific neuroscience without model retraining.

Conclusions:

The developed framework enhances the interpretability of deep learning models in EEG analysis.
It offers a pathway to understand model learning and gain neuroscientific insights.
This work facilitates a deeper connection between artificial intelligence and brain function studies.