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I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks.

Taeho Kang1, Yiyu Chen2, Christian Wallraven2,3

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|September 9, 2024
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Summary

Independent component (IC)-based noise rejection offers minimal benefits for electroencephalography (EEG) decoding using neural networks. Even with advanced methods, IC-based artifact removal did not consistently improve performance, despite high computational costs.

Keywords:
EEGartifact rejectionautomated data pre-processingbrain–computer interfacesdeep learningindependent component analysisneural networks

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Automated preprocessing of electroencephalography (EEG) data is crucial for transparency and reproducibility.
  • Independent component (IC)-based methods are popular for identifying and removing artifacts in EEG.
  • The efficacy of IC-based noise rejection in multivariate scenarios like neural network decoding remains unclear.

Purpose of the Study:

  • To investigate the impact of IC-based noise rejection on neural network classifier-based decoding of EEG data.
  • To compare different IC decomposition and component rejection strategies across diverse EEG datasets.
  • To evaluate the trade-off between performance gains and computational cost of IC-based preprocessing.

Main Methods:

  • Applied two IC decomposition methods (Infomax, AMICA) and three rejection strategies (none, ICLabel, MARA).
  • Utilized three EEG datasets: motor imagery, long-term memory, and visual memory.
  • Cross-validated processed data with three neural network architectures (two CNNs, one LSTM).

Main Results:

  • IC-based noise rejection provided at best minor benefits for EEG decoding.
  • Component-rejected data did not consistently outperform data without rejection.
  • Significant computational resources are required for IC analysis, questioning the cost-benefit ratio.

Conclusions:

  • The utility of IC-based noise rejection in neural network-based EEG decoding is questionable.
  • Current IC-based artifact removal methods may not justify the computational expense for decoding tasks.
  • Further research may be needed to optimize automated EEG preprocessing for multivariate analyses.