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Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations

Pierre Guetschel, Sara Ahmadi, Michael Tangermann

TL;DR

It is observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but there are also more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data.

Abstract

In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest. This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art. Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations. Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data. Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.

Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations

TL;DR

It is observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but there are also more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data.

Abstract

In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest. This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art. Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations. Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data. Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models.
Paper Structure (42 sections, 2 figures)

This paper contains 42 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Our Methodology
  3. Motivations to learn embeddings
  4. Improve classification accuracy
  5. Learning to become robust to noise
  6. Learning invariances
  7. Learning from small datasets
  8. Bridging heterogeneous components
  9. Between different types of algorithms.
  10. Between different data types.
  11. Between different recording systems.
  12. To enforce multiple objectives.
  13. Uncover the structure of the data
  14. Approaches used to obtain embeddings
  15. Supervised methods.
  16. ...and 27 more sections

Figures (2)

  • Figure 1: Flow diagram summarizing the process for selecting which articles from the initial search results to consider in this review. Blue boxes indicate the initial available sources and number of articles, red boxes indicate articles which were not considered for various reasons (see main text), and the green box represents the finally considered articles.
  • Figure 2: Example of -projected visualisation of embeddings. Figure description. In this figure, each point corresponds to one embedding vector. Its color denotes the class labels. The sub-plots depict embedding vectors obtained for different subjects, but all vectors were generated by the same embedding function. The sub-plot of test subject 1 is marked by a red frame. The topographic isolines in the background indicate the four class distributions as derived from the complete data of all subjects. The plots of only three subjects are displayed here for space reasons. Comments. This plot was used to realize that overall, the features learned were relevant for the targeted classification task, even for the test subject. Additionally, it indicated a hierarchy in the difficulty to separate the different pairs of classes. The topographic isolines in the background served as a visual reference to compare sub-plots and allowed to observe distribution shifts between the embeddings of different subjects. Source: Guetschel et al. 2022 GuePapTan22.