Self-supervised learning has emerged as a highly effective approach in the
fields of natural language processing and computer vision. It is also
applicable to brain signals such as electroencephalography (EEG) data, given
the abundance of available unlabeled data that exist in a wide spectrum of
real-world medical applications ranging from seizure detection to wave
analysis. The existing works leveraging self-supervised learning on EEG
modeling mainly focus on pretraining upon each individual dataset corresponding
to a single downstream task, which cannot leverage the power of abundant data,
and they may derive sub-optimal solutions with a lack of generalization.
Moreover, these methods rely on end-to-end model learning which is not easy for
humans to understand. In this paper, we present a novel EEG foundation model,
namely EEGFormer, pretrained on large-scale compound EEG data. The pretrained
model cannot only learn universal representations on EEG signals with adaptable
performance on various downstream tasks but also provide interpretable outcomes
of the useful patterns within the data. To validate the effectiveness of our
model, we extensively evaluate it on various downstream tasks and assess the
performance under different transfer settings. Furthermore, we demonstrate how
the learned model exhibits transferable anomaly detection performance and
provides valuable interpretability of the acquired patterns via self-supervised
learning.
Self-supervised learning has gained popularity in natural language processing, computer vision, and now even in analyzing brain signals such as electroencephalography (EEG) data. The availability of vast amounts of unlabeled EEG data in medical applications makes self-supervised learning a promising approach for various tasks like seizure detection and wave analysis. However, existing methods in this field have their limitations.
Most previous works in self-supervised learning on EEG data focus on pretraining models on individual datasets for specific downstream tasks. This approach fails to fully leverage the potential of the abundant data available and may result in sub-optimal solutions that lack generalization. Additionally, these models often rely on end-to-end learning, making it challenging for humans to understand the underlying mechanisms.
In this paper, the authors introduce a new EEG foundation model called EEGFormer. This model is pretrained on a large-scale compound EEG dataset, enabling it to learn universal representations of EEG signals and adapt its performance to various downstream tasks. Not only does EEGFormer exhibit adaptable performance, but it also offers interpretable outcomes by extracting useful patterns from the data.
To demonstrate the effectiveness of EEGFormer, the authors extensively evaluate its performance on multiple downstream tasks and assess its transferability in different settings. Moreover, they showcase how the learned model can successfully detect anomalies and provide valuable interpretability through self-supervised learning.
The concepts discussed in this paper exemplify the multidisciplinary nature of multimedia information systems. The integration of self-supervised learning into the analysis of brain signals expands the applications of multimedia technologies beyond visual and textual data. By leveraging large-scale EEG datasets, researchers contribute to the field of artificial reality by enabling more accurate and interpretable interactions between humans and machines.
This work also has implications for animations and virtual realities. As more immersive experiences are being developed in these domains, understanding and interpreting brain signals becomes crucial. The EEGFormer model’s ability to uncover meaningful patterns and detect anomalies can enhance the immersive experiences and improve user engagement in animations, virtual realities, and augmented reality applications.
In conclusion, this paper presents an innovative approach to EEG data analysis through self-supervised learning. The EEGFormer model not only achieves adaptable performance on various tasks but also provides interpretable outcomes, making it a valuable tool in the field of multimedia information systems and its related disciplines.