Electroencephalogram (EEG) is one of the most reliable physiological signal for emotion detection. Being non-stationary in nature, EEGs are better analysed by spectro temporal representations. Standard features like Discrete Wavelet Transformation (DWT) can represent temporal changes in spectral dynamics of an EEG, but is insufficient to extract information other way around, i.e. spectral changes in temporal dynamics. On the other hand, Empirical mode decomposition (EMD) based features can be useful to bridge the above mentioned gap. Towards this direction, we extract two novel features on top of EMD, namely, (a) marginal hilbert spectrum (MHS) and (b) Holo-Hilbert spectral analysis (HHSA) based on EMD, to better represent emotions in 2D arousal-valence (A-V) space. The usefulness of these features for EEG emotion classification is investigated through extensive experiments using state-of-the-art classifiers. In addition, experiments conducted on DEAP dataset for binary emotion classification in both A-V space, reveal the efficacy of the proposed features over the standard set of temporal and spectral features.
RIKEN AIP（理化学研究所先进智能项目中心）；Graz University of Technology（格拉茨工业大学）
Riemannian tangent space methods offer state-of-the-art performance in magnetoencephalography (MEG) and electroencephalography (EEG) based applications such as brain-computer interfaces and biomarker development. One limitation, particularly relevant for biomarker development, is limited model interpretability compared to established component-based methods. Here, we propose a method to transform the parameters of linear tangent space models into interpretable patterns. Using typical assumptions, we show that this approach identifies the true patterns of latent sources, encoding a target signa…
Background: Depression has become a major health burden worldwide, and effective detection depression is a great public-health challenge. This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Methods: Resting state EEG data was collected from 24 major depressive patients (MDD) and 29 normal controls using 128 channel HydroCel Geodesic Sensor Net (HCGSN). To better identify depression, we extracted different types of EEG features including linear features, nonlinear features and functional connectivity features phase lagging index (P…
Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection
USD AI Research, Department of Computer Science, University of South Dakota（USD人工智能研究机构、计算机科学系、南达科他大学）；Biomedical and Translational Sciences, Sanford School of Medicine, University of South Dakota（生物医学与转化科学、桑福德医学院、南达科他大学）；Department of Sport, Exercise and Rehabilitation, Northumbria University（体育、运动与康复系、北umbria大学）；Department of Neurology, Oregon Health & Science University（神经病学系、俄勒冈健康与科学大学）
KU Leuven（鲁汶大学）；Leuven Brain Institute（鲁汶脑科学研究所）；Maastricht UMC+（马斯特里赫特大学医学中心+）；University Hospitals Leuven（鲁汶大学医院）；Delft University of Technology（代尔夫特理工大学）
EEG-correlated fMRI analysis is widely used to detect regional blood oxygen level dependent fluctuations that are significantly synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, such an asymmetrical, mass-univariate approach cannot capture the inherent, higher order structure in the EEG data, nor multivariate relations in the fMRI data, and it is nontrivial to accurately handle varying neurovascular coupling over patients and brain regions. We aim to overcome these drawbacks in a data-driven manner by means of a novel stru…
DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
Hanoi University of Science and Technology（河内科学与技术大学）；VNU University of Engineering and Technology（越南工程与技术大学）；Central Military Hospital 108（第108中心军医院）；University of California, Irvine（加州大学尔湾分校）
EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration
Department of Computer Science, Oslo Metropolitan University（计算机科学系，奥斯陆 Metropolitan 大学）；King Abdullah University of Science and Technology（国王 Abdullah 科学与技术大学）；Department of Informatics, University of Oslo（信息学系，奥斯陆大学）；Kristiania University of Applied Sciences（Kristiania 应用科学大学）
Alzheimers disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive a formal diagnosis. Thus, there is a need for accurate, low-cost, and easy-to-use biomarkers that could be used to detect AD in its early stages. Potentially, electroencephalogram (EEG) based …
SCCN, INC, UCSD（加州大学圣迭戈分校斯沃茨计算神经科学中心）；CerCo CNRS, Paul Sabatier University（法国国家科学研究中心大脑与认知研究中心，保罗·萨巴捷大学）
Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc. These achievements benefit from the CNNs' outstanding capability to learn discriminative features with deep layers of neuron structures and iterative training process. This has inspired the EEG research community to adopt CNN in performing EEG classification tasks. However, CNNs learned features are not immediately interpretable, causing a lack of understanding of the CNNs' internal working mechanism. To improve CNN …
Locked-in Syndrome patients are often misdiagnosed and face pessimistic prognosis because of similarities with disorders of consciousness, a lack of objective biomarkers and a difficult-to-recognize pathogenesis. Biomarkers show promise in identifying similar conditions, utilizing electroencephalography (EEG) data. This data, particularly in the form of event-related potentials (ERPs), while successful in varying applications, suffers from methodological constraints and interpretation obstacles. The study documented in this body of work explores a machine learning paradigm with regards to N400…
NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University（西溪大学工程学院先进神经技术中心）；School of Data Science, Xiamen University Malaysia（马来西亚厦门大学数据科学学院）；Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第二医院神经外科）；Department of Education and Correction, Zhejiang Gongchen Compulsory Isolated Detoxification Center（浙江省公检强制隔离戒毒所教育矫正部）；Zhejiang Liangzhu Compulsory Isolated Detoxification Center（浙江省良渚强制隔离戒毒所）
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
University 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）；TAU Inria（特拉维夫大学-法国国家信息与自动化研究所联合机构）；LISN-CNRS（法国国家科学研究中心LISN实验室）；Roche Pharma Research and Early Development（罗氏制药研发部）；Roche Innovation Center Basel（罗氏创新中心巴塞尔分部）；F. Hoffmann–La Roche Ltd（罗氏控股有限公司）
University of Rajshahi（拉杰沙希大学）；Hajee Mohammad Danesh Science and Technology University（哈吉·穆罕默德·达内什科技大学）；Deakin University（迪肯大学）；Charles Sturt University（查尔斯·斯特特大学）；COMSATS University Islamabad（伊斯兰堡COMSATS大学）
Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incor…
Convolutional Neural Networks(CNNs) has achieved remarkable performance breakthrough in a variety of tasks. Recently, CNNs based methods that are fed with hand-extracted EEG features gradually produce a powerful performance on the EEG data based emotion recognition task. In this paper, we propose a novel convolutional layer allowing to adaptively extract effective data-driven spectrogram-like features from raw EEG signals, which we reference as scaling layer. Further, it leverages convolutional kernels scaled from one data-driven pattern to exposed a frequency-like dimension to address the sho…
University of Engineering and Technology, Taxila（塔克西拉工程技术大学）；Children’s National Hospital（国家儿童医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫·扎耶德儿科外科创新研究所）；George Washington University（乔治华盛顿大学）
The loss of limb motion arising from damage to the spinal cord is a disability that could effect people while performing their day-to-day activities. The restoration of limb movement would enable people with spinal cord injury to interact with their environment more naturally and this is where a brain-computer interface (BCI) system could be beneficial. The detection of limb movement imagination (MI) could be significant for such a BCI, where the detected MI can guide the computer system. Using MI detection through electroencephalography (EEG), we can recognize the imagination of movement in a…
Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
Stevens Institute of Technology（史蒂文斯理工学院）；Semcer Center for Healthcare Innovation（塞姆瑟医疗创新中心）；New Jersey Institute of Technology（新泽西理工学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University（国立阳明交通大学医学院脑科学研究所）
University of Basel（巴塞尔大学）；Hospital of the University of Basel（巴塞尔大学医院）
Recordings of electrical brain activity carry information about a person's cognitive health. For recording EEG signals, a very common setting is for a subject to be at rest with its eyes closed. Analysis of these recordings often involve a dimensionality reduction step in which electrodes are grouped into 10 or more regions (depending on the number of electrodes available). Then an average over each group is taken which serves as a feature in subsequent evaluation. Currently, the most prominent features used in clinical practice are based on spectral power densities. In our work we consider a …
EEGReXferNet: A Lightweight Gen-AI Framework for EEG Subspace Reconstruction via Cross-Subject Transfer Learning and Channel-Aware Embedding
IUCRC BRAIN Center, Cullen College of Engineering, University of Houston（IUCRC BRAIN中心、工程学院、休斯顿大学）；Department of Computer Engineering, Purdue University（计算机工程系、普渡大学）
