In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach. Specifically, we build our system based on the deep autoencoder architecture which is designed not only to extract discriminant features in the multimodal data representation but also to reconstruct the data from the latent representation using encoder-decoder layers. Since autoencoder can be seen as a compression approach, we extend it to handle multimodal data at the encoder layer, reconstructed and retrieved at the decoder layer. We show through experimental results, that exploiting both multimodal data intercorellation and intracorellation 1) Significantly reduces signal distortion particularly for high compression levels 2) Achieves better accuracy in classifying EEG and EMG signals recorded and labeled according to the sentiments of the volunteer.
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces
University hospital Knappschaftskrankenhaus Bochum GmbH（波鸿克纳普沙夫特医院有限公司大学医院）；Ruhr University Bochum（波鸿鲁尔大学）；Ruhr-West University of Applied Science（鲁尔西部应用科学大学）
A Statistical Mixture-of-Experts Framework for EMG Artifact Removal in EEG: Empirical Insights and a Proof-of-Concept Application
Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）；Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）
ST-Topo GAN: A Motor EEG-to-EMG Decoding Model Matched to Wrist Movement Complexity
Zhengzhou University（郑州大学）；HumVerse (Zhengzhou) Technologies Co., Ltd.（HumVerse（郑州）科技有限公司）；Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology（河南省脑科学与脑机接口技术重点实验室）；Xi’an Jiaotong University（西安交通大学）；University of Electronic Science and Technology of China（电子科技大学）；Chinese Academy of Medical Sciences（中国医学科学院）
A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation
Institute for Applied Computer Science, University of the Bundeswehr Munich（慕尼黑联邦国防军大学应用计算机科学研究所）；brainboost GmbH（brainboost 有限公司）；Graduate School of Engineering Science, The University of Osaka（大阪大学工学研究科）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学新领域创成科学研究科）
Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection
Department One（部门一）；Department Two（部门二）；Institute for Intelligent Systems Research（智能系统研究与创新研究所）；School of Mathematics, Physics and Computing（数学、物理与计算学院）；University of Southern Queensland（南方昆士兰大学）
Targeted Adversarial Denoising Autoencoders (TADA) for Neural Time Series Filtration
Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）；Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）
How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings
brainboost GmbH（brainboost有限公司）；Institute for Applied Computer Science, Bundeswehr University Munich（慕尼黑联邦国防军大学应用计算机科学研究所）；Center for Machine Learning, Heilbronn University（海尔布隆大学机器学习中心）
Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning
Department of Electrical and Electronic Engineering, Imperial College London（帝国理工学院伦敦校区电子与电气工程系）；Dyson School of Design Engineering, Imperial College London（帝国理工学院伦敦校区戴森设计工程学院）
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
Emory University（埃默里大学）；University Hospital Tübingen（图宾根大学医院）；University of Tübingen（图宾根大学）；Technische Universität Dresden（德累斯顿工业大学）；German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心）；FH Aachen University of Applied Sciences（亚琛应用技术大学）
DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models
Xiamen University（厦门大学）；Nanyang Technological University（南洋理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Southeast University（东南大学）
A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input
Eindhoven University of Technology（埃因霍温理工大学）；Philips Sleep and Respiratory Care（飞利浦睡眠与呼吸护理部门）；Sleep Medicine Centre Kempenhaeghe（基姆彭海格睡眠医学中心）；Ulm University（乌尔姆大学）
Geometric-Stochastic Multimodal Deep Learning for Predictive Modeling of SUDEP and Stroke Vulnerability
Dept. of Artificial Intelligence & Machine Learning（人工智能与机器学习系）；B.N.M Institute of Technology（B.N.M技术学院）；Dept. of Computer Science Engineering（计算机科学与工程系）
CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models
Department of Computer Science and Engineering（计算机科学与工程系）；Incheon National University（庆尚国立大学）；Department of Artificial Intelligence（人工智能系）；Inha University（Inha大学）；Center for Brain-Machine Interface（脑机接口中心）
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
Department of Brain and Cognitive Engineering, Korea University（脑科学与认知工程系，韩国大学）；Interdisciplinary Program in Precision Public Health, Korea University（精准公共卫生跨学科项目，韩国大学）；Department of Radiology, Stanford University School of Medicine（放射科，斯坦福大学医学院）；VA Palo Alto Health Care System（帕洛阿尔托医疗系统）；Department of Neurology, Korea University College of Medicine（神经病学系，韩国大学医学院）
