Many studies have explored brain signals during the performance of a memory task to predict later remembered items. However, prediction methods are still poorly used in real life and are not practical due to the use of electroencephalography (EEG) recorded from the scalp. Ear-EEG has been recently used to measure brain signals due to its flexibility when applying it to real world environments. In this study, we attempt to predict whether a shown stimulus is going to be remembered or forgotten using ear-EEG and compared its performance with scalp-EEG. Our results showed that there was no significant difference between ear-EEG and scalp-EEG. In addition, the higher prediction accuracy was obtained using a convolutional neural network (pre-stimulus: 74.06%, on-going stimulus: 69.53%) and it was compared to other baseline methods. These results showed that it is possible to predict performance of a memory task using ear-EEG signals and it could be used for predicting memory retrieval in a practical brain-computer interface.
Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the s…
Ear EEG based driver fatigue monitoring systems have the potential to provide a seamless, efficient, and feasibly deployable alternative to existing scalp EEG based systems, which are often cumbersome and impractical. However, the feasibility of detecting the relevant delta, theta, alpha, and beta band EEG activity through the ear EEG is yet to be investigated. Through measurements of scalp and ear EEG on ten subjects during a simulated, monotonous driving experiment, this study provides statistical analysis of characteristic ear EEG changes that are associated with the transition from alert t…
University of Moratuwa（莫拉图瓦大学）；Harvard University（哈佛大学）；Aarhus University（奥胡斯大学）；Florida International University（佛罗里达国际大学）
Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation …
In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as v…
Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography signals are distorted by movement artifacts and electromyography signals in ambulatory condition, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-EEG has been developed for practical brain-computer interface and is widely used. However, ear-EEG still contains contaminated signals. In this paper, we proposed robust two-stream deep neural networks in walking conditions and analyzed the visual re…
We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0m/s, respectively. For ea…
In universal environment, a patient-friendly inexpensive method is needed to realize the early diagnosis of depression, which is believed to be an effective way to reduce the mortality of depression. The purpose of this study is only to collect EEG signal from three electrodes Fp1, Fpz and Fp2, then the linear and nonlinear features of EEG used to classify depression patients and healthy controls. The EEG recordings were carried out on a group of 18 medication-free depressive patients and 25 gender and age matched controls. In this paper, the selected features include three linear (maximum, me…
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 …
Frankfurt Institute for Advanced Studies (FIAS)（法兰克福高等研究院（FIAS））；Goethe University Frankfurt（法兰克福歌德大学）；Center for Personalized Translational Epilepsy Research (CePTER)（个性化转化癫痫研究中心（CePTER））；Oscar Langendorff Institute of Physiology, Rostock University Medical Center（罗斯托克大学医学中心奥斯卡·朗根多夫生理研究所）；Royal College of Surgeons Ireland（爱尔兰皇家外科医学院）
Epilepsy is one of the most common neurological disorders, affecting about 1% of the population at all ages. Detecting the development of epilepsy, i.e., epileptogenesis (EPG), before any seizures occur could allow for early interventions and potentially more effective treatments. Here, we investigate if modern machine learning (ML) techniques can detect EPG from intra-cranial electroencephalography (EEG) recordings prior to the occurrence of any seizures. For this we use a rodent model of epilepsy where EPG is triggered by electrical stimulation of the brain. We propose a ML framework for EPG…
A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives
The College of Computer Science, Zhejiang University, Hangzhou, China.（浙江大学计算机科学学院）；The MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China.（脑科学与脑机接口前沿科学中心）；The Nanhu Brain-Computer Interface Institute, Hangzhou, China.（南湖脑机接口研究院）；The Qiushi Academy for Advanced Studies, Hangzhou, China.（启石先进研究院）
Epilepsy is a neurological condition such that it affects the brain and the nervous system. It is characterized by recurrent seizures, which are physical reactions to sudden, usually brief, excessive electrical discharges in a group of brain cells. Hence, seizure identification has great importance in clinical therapy of epileptic patients. Electroencephalogram (EEG) is one of the main biomarker that can measure voltage fluctuations of the brain and EEG data analysis helps to investigate the patient with epilepsy syndrome as epilepsy leaves their signature in EEG signals. In this paper, the Di…
The applications of Electroencephalogram (EEG) have been extended to out of laboratory and clinics recently due to the advancements in the technical capabilities. There are various advantageous of EEG, making it a preferable method for a wide range of applications; it is a noninvasive method, it is portable, it offers good time resolution and sufficient spatial resolution, besides there are low cost EEG systems available for a commercial use. Since the early uses of EEG, mainly as monitoring of diseases and pathologies, sleep staging and event related potential researches, it has been intertwi…
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
École de technologie supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Dibrugarh University（Dibrugarh大学）；Vytautas Magnus University（维陶塔斯马格努斯大学）；University of Southern Queensland（南昆士兰大学）
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…
Baylor College of Medicine（贝勒医学院）；Michael E. DeBakey VA Medical Center（迈克尔·E·德贝基退伍军人事务医疗中心）
The Early diagnosis and treatment of depression is essential for effective treatment. Depression, while being one of the most common mental illnesses, is still poorly understood in both research and clinical practice. Among different treatments, drug prescription is widely used, however the drug treatment is not effective for many patients. In this work, we propose a method for major depressive disorder (MDD) diagnosis as well as a method for predicting the drug response in patient with MDD using EEG signals. Method: We employ transformers, which are modified recursive neural networks with nov…
Monash University（莫纳什大学）；Alfred Health（阿尔弗雷德健康）；The University of Melbourne（墨尔本大学）；Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）；St. Vincent’s Hospital, The University of Melbourne（墨尔本大学圣文森特医院）；Turner Institute for Brain and Mental Health, Monash University（莫纳什大学特纳脑与心理健康研究所）；Monash Biomedical Imaging, Monash University（莫纳什大学生物医学成像）；Wellcome Centre for Human Neuroimaging, UCL（伦敦大学学院人类神经成像威尔康姆中心）
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains…
Auditory Attention Decoding without Spatial Information: A Diotic EEG Study
Department of Electronic and Information Engineering, School of Engineering, The University of Osaka, Suita, Japan（大阪大学工学部电子信息工学科）；Graduate School of Engineering, The University of Osaka, Suita, Japan（大阪大学工学研究院）
Diffusion-based translation between unpaired spontaneous premature neonatal EEG and fetal MEG
Laboratoire de Mathématiques de Lens (LML), UR 2462, Université d'Artois（Lens数学实验室（LML），UR 2462，阿托瓦大学）；Department of Internal Medicine IV, University Hospital of Tübingen（第四内科部门，图宾根大学医院）；IDM/fMEG Center of the Helmholtz Center Munich, University of Tübingen, German Center for Diabetes Research (DZD)（慕尼黑海德堡中心IDM/fMEG中心，图宾根大学，糖尿病研究德国中心（DZD））；Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Interfaculty Centre for Pharmacogenomics and Pharma Research, Eberhard Karls University Tübingen（药学研究所，药学与生物化学部门，药基因组学与药学研究跨学科中心，图宾根埃伯哈德·卡尔斯大学）
CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
State Key Laboratory of Brain-machine Intelligence（脑机智能国家重点实验室）；College of Computer Science and Technology（计算机科学与技术学院）；Alibaba Group（阿里巴巴集团）；Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine（神经生物学系、附属心理健康中心及杭州第七人民医院，浙江大学医学院）；MOE Frontier Science Center for Brain Science and Brain-machine Integration（教育部脑科学与脑机集成前沿科学中心）
