Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering. In this paper we firstly review recently emerging studies on generating artificial electroencephalography (EEG) signals with deep neural networks. Subsequently, we present our feasibility experiments on generating condition-specific multichannel EEG signals using conditional variational autoencoders. By manipulating real resting-state EEG epochs, we present an approach to synthetically generate time-series multichannel signals that show spectro-temporal EEG patterns which are expected to be observed during distinct motor imagery conditions.
Higher School of Economics（高等经济学院）；Artificial Intelligence Research Institute, AIRI（人工智能研究所（AIRI））
The access to activity of subcortical structures offers unique opportunity for building intention dependent brain-computer interfaces, renders abundant options for exploring a broad range of cognitive phenomena in the realm of affective neuroscience including complex decision making processes and the eternal free-will dilemma and facilitates diagnostics of a range of neurological deceases. So far this was possible only using bulky, expensive and immobile fMRI equipment. Here we present an interpretable domain grounded solution to recover the activity of several subcortical regions from the mul…
Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks
Communication Engineering Department National Engineering School of Tunis Tunis El Manar University；REGIM-Lab, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038, Tunisia National Engineering School of Tunis Tunis El Manar University
Epilepsy is a chronic neurological disorder affecting 1\% of people worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for accurate epileptic seizure (ES) prediction, thereby benefiting patients suffering from epilepsy. To identify the preictal region that precedes the onset of seizure, a large number of annotated EEG signals are required to train DL algorithms. However, the scarcity of seizure onsets leads to significant insufficiency of data for training the DL algorithms. To overcome this data insufficiency, in this paper, we propose…
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学电子计算学系）
The data scarcity problem in Electroencephalography (EEG) based affective computing results into difficulty in building an effective model with high accuracy and stability using machine learning algorithms especially deep learning models. Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting. In this paper, we propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER). As the first to combine adversarial training with …
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals
National Tsing Hua University（台湾清华大学）；ELAN Microelectronics Corporation（义隆电子股份有限公司）；National Yang Ming Chiao Tung University（阳明交通大学）；National Taipei University of Technology（台北科技大学）
A Deep Generative Model for Resting-State EEG Synthesis and Transferable Representation Learning
Institute of Psychology, Eötvös Loránd University（埃斯特哈兹·洛朗大学心理学研究所）；Doctoral School of Psychology, Eötvös Loránd University（埃斯特哈兹·洛朗大学心理学博士学院）；Department of Behavioural and Cognitive Sciences, University of Luxembourg（卢森堡大学行为与认知科学系）
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）
Frequency-Specific Neural Response and Cross-Correlation Analysis of Envelope Following Responses to Native Speech and Music Using Multichannel EEG Signals: A Case Study
Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology（电气与电子工程系，库尔纳大学工程与技术学院）；School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University（电气工程、计算与数学科学学院， Curtin 大学）
In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by st…
The University of Western Australia（西澳大利亚大学）；Effat University（埃法特大学）
Electroencephalography is frequently used for diagnostic evaluation of various brain-related disorders due to its excellent resolution, non-invasive nature and low cost. However, manual analysis of EEG signals could be strenuous and a time-consuming process for experts. It requires long training time for physicians to develop expertise in it and additionally experts have low inter-rater agreement (IRA) among themselves. Therefore, many Computer Aided Diagnostic (CAD) based studies have considered the automation of interpreting EEG signals to alleviate the workload and support the final diagnos…
Tokyo University of Agriculture and Technology（东京农工大学）；Juntendo University Nerima Hospital（顺天堂大学练马医院）
Electroencephalography (EEG) is essential for the diagnosis of epilepsy, but it requires expertise and experience to identify abnormalities. It is thus crucial to develop automated models for the detection of abnormalities in EEGs related to epilepsy. This paper describes the development of a novel class of compact convolutional neural networks (CNNs) for detecting abnormal patterns and electrodes in EEGs for epilepsy. The designed model is inspired by a CNN developed for brain-computer interfacing called multichannel EEGNet (mEEGNet). Unlike the EEGNet, the proposed model, mEEGNet, has the sa…
Objective: The detection of epileptic seizures from scalp electroencephalogram (EEG) signals can facilitate early diagnosis and treatment. Previous studies suggested that the Gaussianity of EEG distributions changes depending on the presence or absence of seizures; however, no general EEG signal models can explain such changes in distributions within a unified scheme. Methods: This paper describes the formulation of a stochastic EEG model based on a multivariate scale mixture distribution that can represent changes in non-Gaussianity caused by stochastic fluctuations in EEG. In addition, we pr…
Deep learning networks are increasingly attracting attention in various fields, including electroencephalography (EEG) signal processing. These models provided comparable performance with that of traditional techniques. At present, however, lacks of well-structured and standardized datasets with specific benchmark limit the development of deep learning solutions for EEG denoising. Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing deep learning-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 …
PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal Synthesis
Objective: This study proposes a new parametric TF (time frequency) CGC (conditional Granger causality) method for high precision connectivity analysis over time and frequency in multivariate coupling nonstationary systems, and applies it to scalp and source EEG signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks. Methods: The Geweke spectral measure is combined with the TVARX (time varying autoregressive with exogenous input) modelling approach, which uses multiwavelets and ultra regularized orthogonal least squares (UROLS) algorithm aided by APRESS…
Automated seizure detection using clinical electroencephalograms is a challenging machine learning problem because the multichannel signal often has an extremely low signal to noise ratio. Events of interest such as seizures are easily confused with signal artifacts (e.g, eye movements) or benign variants (e.g., slowing). Commercially available systems suffer from unacceptably high false alarm rates. Deep learning algorithms that employ high dimensional models have not previously been effective due to the lack of big data resources. In this paper, we use the TUH EEG Seizure Corpus to evaluate …
Optimizing Brain-Computer Interface Performance: Advancing EEG Signals Channel Selection through Regularized CSP and SPEA II Multi-Objective Optimization
Center for Translational Research in Neuroimaging and Data Science (TReNDS)（神经成像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；K.N Toosi University of Technology（K.N. 图西理工大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation
School of Intelligence Science and Technology, University of Science and Technology Beijing（信息科学与技术学院，北京科技大学）；School of Computer and Communication Engineering, University of Science and Technology Beijing（计算机与通信工程学院，北京科技大学）
EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals
Zhejiang University（浙江大学）；Qiushi Academy for Advanced Studies, Zhejiang University（浙江大学求是高等研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The Hong Kong Polytechnic University（香港理工大学）；Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University（浙江大学教育部生物医学工程重点实验室）
University of New South Wales（新南威尔士大学）；Xi’an Jiaotong University（西安交通大学）
Synthesizing geometrical shapes from human brain activities is an interesting and meaningful but very challenging topic. Recently, the advancements of deep generative models like Generative Adversarial Networks (GANs) have supported the object generation from neurological signals. However, the Electroencephalograph (EEG)-based shape generation still suffer from the low realism problem. In particular, the generated geometrical shapes lack clear edges and fail to contain necessary details. In light of this, we propose a novel multi-task generative adversarial network to convert the individual's …
Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal, which makes the systems inherently non-causal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance. The p…
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.（自动化科学与电气工程学院，北航，北京，中国）；School of Biological Science and Medical Engineering, Beihang University, Beijing, China.（生物科学与医学工程学院，北航，北京，中国）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.（虚拟现实技术与系统国家重点实验室，北航，北京，中国）；T Magnetic Resonance Imaging Translational Medical Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.（7T磁共振成像转化医学中心，放射科，西南医院，军医大学（第三军医大学），重庆，中国）
Old Dominion University（欧道明大学）；Jefferson Laboratory（杰斐逊实验室）
Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, th…
