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Beyond averaging: A transformer approach to decoding event related brain potentials.

Philipp Zelger1, Manuel Arnold2, Sonja Rossi1

  • 1University Hospital for Hearing, Speech & Voice Disorders, Medical University of Innsbruck, Anichstrasse 35, Innsbruck, 6020, Austria; ICONE - Innsbruck Cognitive Neuroscience, Medical University of Innsbruck, Anichstrasse 35, Innsbruck, 6020, Austria.

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|January 26, 2025
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Summary

Transformer deep learning with attention maps offers deeper insights into electroencephalographic (EEG) event-related potentials (ERPs) than traditional averaging. This approach reveals crucial neural signal timings, enhancing ERP analysis.

Keywords:
Deep learningEEGEvent related potentialsLoudness perceptionTransformer

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Traditional analysis of electroencephalographic (EEG) data for event-related potentials (ERPs) relies on signal averaging, which can obscure important neural information.
  • Deep learning, particularly transformer networks with attention mechanisms, presents a novel approach to analyze complex EEG data.

Purpose of the Study:

  • To evaluate the efficacy of a transformer-based deep learning model in analyzing ERPs derived from EEG data.
  • To compare the insights gained from transformer attention maps against traditional averaging methods for ERP analysis.
  • To investigate neural responses related to loudness perception using advanced deep learning techniques.

Main Methods:

  • A convolutional transformer model was trained on EEG data from 29 participants during a loudness perception experiment.
  • Participants rated acoustic stimuli (65-95 dB) as 'too loud' or 'not too loud' while their EEG was recorded.
  • Attention maps were generated from the trained transformer to identify relevant time windows in the EEG data.

Main Results:

  • The transformer model achieved high classification accuracy (>86%) and an Area Under the Curve (AUC) of up to 0.95 for distinguishing loudness categories.
  • Attention maps highlighted specific time windows (around 150-200 ms) crucial for classification, which were not evident in traditional averaging analyses.
  • These findings suggest that attention maps can reveal subtle neural patterns missed by conventional methods.

Conclusions:

  • Transformer-based deep learning, utilizing attention maps, provides a powerful and nuanced method for analyzing ERPs from EEG data.
  • This approach offers superior insights into neural processing compared to traditional averaging techniques, particularly in identifying key temporal dynamics.
  • Attention maps demonstrate significant potential as a tool for deeper and more comprehensive ERP analysis in neuroscience research.