Electroencephalography (EEG) plays a crucial role in brain-computer interfaces (BCIs) and neurological diagnostics, but its real-world deployment faces challenges due to noise artifacts, missing data, and high annotation costs. We introduce SSL-SE-EEG, a framework that integrates Self-Supervised Learning (SSL) with Squeeze-and-Excitation Networks (SE-Nets) to enhance feature extraction, improve noise robustness, and reduce reliance on labeled data. Unlike conventional EEG processing techniques, SSL-SE-EEG} transforms EEG signals into structured 2D image representations, suitable for deep learning. Experimental validation on MindBigData, TUH-AB, SEED-IV and BCI-IV datasets demonstrates state-of-the-art accuracy (91% in MindBigData, 85% in TUH-AB), making it well-suited for real-time BCI applications. By enabling low-power, scalable EEG processing, SSL-SE-EEG presents a promising solution for biomedical signal analysis, neural engineering, and next-generation BCIs.
Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning to reconstruct the masked EEG features using a transformer architecture. We found that MAEEG can learn representations that significantly improve sleep stage classification (~5% accuracy increase) when only a small number of labels are given. We also found that input sample lengths and differ…
Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；InteraXon Inc.（InteraXon公司）；University of Helsinki（赫尔辛基大学）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）
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 …
EEG signals are usually simple to obtain but expensive to label. Although supervised learning has been widely used in the field of EEG signal analysis, its generalization performance is limited by the amount of annotated data. Self-supervised learning (SSL), as a popular learning paradigm in computer vision (CV) and natural language processing (NLP), can employ unlabeled data to make up for the data shortage of supervised learning. In this paper, we propose a self-supervised contrastive learning method of EEG signals for sleep stage classification. During the training process, we set up a pret…
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Korea University（高丽大学）；University of Cambridge（剑桥大学）；Stanford University School of Medicine（斯坦福大学医学院）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗系统）；Korea University College of Medicine（高丽大学医学院）
Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction
Department of Medical Informatics（医学信息学系）；UCLA（美国加利福尼亚大学洛杉矶分校）；Department of Cognitive Science（认知科学系）；UCSD（美国圣地亚哥大学）；David Geffen School of Medicine（大卫·盖弗医学院）；Mattel Children’s Hospital（马特尔儿童医院）
Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets
University of Tübingen（蒂宾根大学）；Hertie Institute for AI in Brain Health (Hertie AI)（赫蒂脑健康人工智能研究所）；University Clinic Tübingen（蒂宾根大学医院）；Hertie Institute for Clinical Brain Research（赫蒂临床脑研究所）
The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to possessing a massive amount of labeled data for training, limiting their applicability in real-world scenarios. In such scenarios, sleep labs can generate a massive amount of data, but labeling these data can be expensive and time-consuming. Recently, the self-supervised learning (SSL) paradigm has shined as one of the most successful techniques to overcome the scarcity of labeled data. In this paper, we evaluate the effic…
Multimodal Functional Maximum Correlation for Emotion Recognition
Key Laboratory of Child Development and Learning Science (Ministry of Education), School of Biological Sciences and Medical Engineering, Southeast University（儿童发展与学习科学重点实验室（教育部）、生物科学与医学工程学院、东南大学）；Department of Artificial Intelligence, Westlake University（人工智能学院、西湖大学）；Department of Artificial Intelligence, Vrije Universiteit Amsterdam（人工智能学院、阿姆斯特丹自由大学）
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Self-supervised learning (SSL), as a newly emerging unsupervised representation learning paradigm, generally follows a two-stage learning pipeline: 1) learning invariant and discriminative representations with auto-annotation pretext(s), then 2) transferring the representations to assist downstream task(s). Such two stages are usually implemented separately, making the learned representation learned agnostic to the downstream tasks. Currently, most works are devoted to exploring the first stage. Whereas, it is less studied on how to learn downstream tasks with limited labeled data using the al…
SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Massachusetts Institute of Technology（麻省理工学院）；OpenEvidence, USA（OpenEvidence（美国））；New York University（纽约大学）；Xi’an Jiaotong University（西安交通大学）；University of Toronto（多伦多大学）；Emory University（埃默里大学）
