Uncovering the structure of clinical EEG signals with self-supervised learning (2007.16104v1)

Abstract: Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approaches. However, in most situations, unlabeled data is available in abundance. By extracting information from this unlabeled data, it might be possible to reach competitive performance with deep neural networks despite limited access to labels. Approach. We investigated self-supervised learning (SSL), a promising technique for discovering structure in unlabeled data, to learn representations of EEG signals. Specifically, we explored two tasks based on temporal context prediction as well as contrastive predictive coding on two clinically-relevant problems: EEG-based sleep staging and pathology detection. We conducted experiments on two large public datasets with thousands of recordings and performed baseline comparisons with purely supervised and hand-engineered approaches. Main results. Linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available. Additionally, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena, such as age effects. Significance. We demonstrate the benefit of self-supervised learning approaches on EEG data. Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.

• The paper introduces self-supervised learning to effectively leverage unlabeled EEG data for improved sleep staging and pathology detection.
• It employs temporal context prediction and contrastive predictive coding, achieving balanced accuracies of 72.3% to 79.4% on benchmark datasets.
• The study reveals that SSL-trained features capture intrinsic EEG structures linked to sleep stages, patient age, and gender in latent representations.

Uncovering the Structure of Clinical EEG Signals with Self-Supervised Learning

The paper "Uncovering the structure of clinical EEG signals with self-supervised learning" explores the application of self-supervised learning (SSL) to electroencephalography (EEG) data. This paper addresses a significant challenge in the domain of EEG analysis: the scarcity of labeled data. EEG signal annotation is a labor-intensive process that necessitates expertise, making it difficult to obtain large volumes of labeled data required for supervised learning approaches. The authors propose the use of SSL to leverage the abundant availability of unlabeled EEG data and improve the performance of deep learning models.

Key Contributions and Approach

The paper investigates SSL as a strategy to learn useful representations of EEG signals in the absence of labeled data. The paper is structured around two primary tasks: sleep staging and pathology detection. These clinically relevant tasks were chosen due to their critical role in neurological assessments and monitoring.

The authors implement two SSL methods inspired by temporal context prediction and contrastive predictive coding (CPC). The temporal context prediction involves two specific tasks: relative positioning (RP) and temporal shuffling (TS), which exploit the temporal correlations in EEG data to learn representations. CPC, on the other hand, aims to predict future data points in a latent representation space, which aligns with the temporal dependencies inherent in EEG signals.

Two public datasets, Physionet Challenge 2018 (PC18) and TUH Abnormal EEG, were used to validate the proposed methods. These datasets encompass thousands of recordings, allowing for a comprehensive evaluation of SSL methods.

Results

The paper shows that SSL-trained features with linear classifiers consistently outperform purely supervised models, especially in scenarios with limited labeled data. Specifically, on the PC18 dataset, SSL methods achieved a balanced accuracy of up to 72.3% for sleep staging with only minimal labeled data. On the TUH Abnormal dataset for pathology detection, SSL reached a balanced accuracy of 79.4%, indicating robust performance in identifying pathological EEGs.

The embeddings obtained demonstrate clear latent structures related to physiological and clinical phenomena, such as sleep stages, patient age, and gender. For example, SSL representations captured the continuum in sleep stages and age-related variations, providing an insight that is often obscured in discrete classification tasks.

Implications and Speculation

The implications of this research are profound for the field of clinical neuroscience and EEG-based diagnostics. By demonstrating the capacity of SSL to extract meaningful structures from unlabeled EEG data, the paper points towards a future where the reliance on manually labeled data can be significantly reduced. This paradigm shift could facilitate the development of more data-efficient models that retain high performance across diverse EEG tasks.

From a broader AI and machine learning perspective, the paper signifies an advance in SSL methodologies, particularly in applying these techniques to time-series data like EEG. This can inspire similar applications across other domains where labeled data is scarce or costly to obtain.

Conclusion

The paper "Uncovering the structure of clinical EEG signals with self-supervised learning" highlights the potential of SSL in overcoming current limitations in EEG data analysis. By leveraging abundant unlabeled data, SSL can serve as a transformative approach in clinical settings, leading to more efficient and potentially more accurate EEG examinations. Future research could expand these methods to other modalities and explore enhancements in model architectures to further capitalize on the benefits of SSL in biomedical signal processing.