Electroencephalography (EEG) research typically focuses on tasks with narrowly defined objectives, but recent studies are expanding into the use of unlabeled data within larger models, aiming for a broader range of applications. This addresses a critical challenge in EEG research. For example, Kostas et al. (2021) show that self-supervised learning (SSL) outperforms traditional supervised methods. Given the high noise levels in EEG data, we argue that further improvements are possible with additional preprocessing. Current preprocessing methods often fail to efficiently manage the large data volumes required for SSL, due to their lack of optimization, reliance on subjective manual corrections, and validation processes or inflexible protocols that limit SSL. We propose a Python-based EEG preprocessing pipeline optimized for self-supervised learning, designed to efficiently process large-scale data. This optimization not only stabilizes self-supervised training but also enhances performance on downstream tasks compared to training with raw data.
Khulna University of Engineering & Technology（库尔纳工程技术大学）
People undergoing neuromuscular dysfunctions and amputated limbs require automatic prosthetic appliances. In developing such prostheses, the precise detection of brain motor actions is imperative for the Grasp-and-Lift (GAL) tasks. Because of the low-cost and non-invasive essence of Electroencephalography (EEG), it is widely preferred for detecting motor actions during the controls of prosthetic tools. This article has automated the hand movement activity viz GAL detection method from the 32-channel EEG signals. The proposed pipeline essentially combines preprocessing and end-to-end detection …
DREAMS: A python framework for Training Deep Learning Models on EEG Data with Model Card Reporting for Medical Applications
Department of Computer Science, OsloMet -- Oslo Metropolitan University（计算机科学系，奥斯陆Met大学）；Simula Research Laboratory, Numerical Analysis and Scientific Computing（Simula研究实验室，数值分析与科学计算）
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
EEG Foundation Models: A Critical Review of Current Progress and Future Directions
Department of Computer Science & Engineering, University of Minnesota Twin Cities（计算机科学与工程系，明尼苏达大学双城分校）；Department of Bioengineering, University of Illinois at Urbana-Champaign（生物工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Neurology, Mayo Clinic（神经病学系，梅奥诊所）；Department of Neurology, University of Minnesota Twin Cities（神经病学系，明尼苏达大学双城分校）
Spatiotemporal EEG-Based Emotion Recognition Using SAM Ratings from Serious Games with Hybrid Deep Learning
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences（计算机科学、电气工程与数学科学系，西挪威应用科学大学）
A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
Computer Science Department, College of Science, University of Zakho（扎赫大学科学学院计算机科学系）；Department of Computer Science and Information Technology, The American University of Kurdistan（库尔德斯坦美国大学计算机科学与信息技术系）；PRIME Lab, Scientific Research Center, University of Zakho（扎赫大学科学研究中心PRIME实验室）
Deep Learning CNN and Recurrence Analysis for Alpha Gamma EEG Biomarkers in Fragile X Syndrome
School of Information Technology (SoIT), University of Cincinnati（辛辛那提大学信息技术学院）；Division of Child and Adolescent Psychiatry Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医学中心儿童与青少年精神病学科）
University of Rome Tor Vergata（罗马第二大学）；Drexel University（德雷塞尔大学）；University of Pisa（比萨大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
The brain computer interface (BCI) is a nonstimulatory direct and occasionally bidirectional communication link between the brain and a computer or an external device. Classically, EEG-based BCI algorithms have relied on models such as support vector machines and linear discriminant analysis or multiclass common spatial patterns. During the last decade, however, more sophisticated machine learning architectures, such as convolutional neural networks, recurrent neural networks, long short-term memory networks and gated recurrent unit networks, have been extensively used to enhance discriminabil…
A Machine Learning Framework for EEG-Based Prediction of Treatment Efficacy in Chronic Neck Pain
Department of Kinesiology, Faculty of Science, McMaster University（麦吉尔大学运动科学系）；W Booth School of Engineering Practice and Technology, Faculty of Engineering, McMaster University（麦吉尔大学工程学院W Booth工程实践与技术学院）
The recorded Electroencephalography (EEG) data comes with a large size due to the high sampling rate. Therefore, large space and more bandwidth are required for storing and transmitting the EEG data. Thus, preprocessing and compressing the EEG data is a very important part in order to transmit and store it efficiently with less bandwidth and less space. The objective of this paper is to develop an efficient system for EEG data compression. In this system, the recorded EEG data are firstly preprocessed in the preprocessing unit. Standardization and segmentation of EEG data are done in this unit…
Indian Institute of Technology Gandhinagar（印度理工学院甘地纳格尔校区）；Delft University of Technology（代尔夫特理工大学）
This research study aims to use machine learning methods to characterize the EEG response to music. Specifically, we investigate how resonance in the EEG response correlates with individual aesthetic enjoyment. Inspired by the notion of musical processing as resonance, we hypothesize that the intensity of an aesthetic experience is based on the degree to which a participants EEG entrains to the perceptual input. To test this and other hypotheses, we have built an EEG dataset from 20 subjects listening to 12 two minute-long songs in random order. After preprocessing and feature construction, we…
In this study, we validate the findings of previously published papers, showing the feasibility of an Electroencephalography (EEG) based gaze estimation. Moreover, we extend previous research by demonstrating that with only a slight drop in model performance, we can significantly reduce the number of electrodes, indicating that a high-density, expensive EEG cap is not necessary for the purposes of EEG-based eye tracking. Using data-driven approaches, we establish which electrode clusters impact gaze estimation and how the different types of EEG data preprocessing affect the models' performance…
Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computational burden required for processing lengthy MI-EEG signals. In this paper, we propose an unsupervised method based on the self-attention mechanism to detect the salient intervals of MI-EEG signals automatically. Our suggested method can be used as a preprocessing step within any BCI algorithm to enhance its performance. The effectiveness of the suggested method is evaluated on the most widely used BCI algorithm, the …
Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
School of Computer Science, Technological University Dublin, Dublin, Ireland（计算机科学学院，都柏林技术大学，都柏林，爱尔兰）；Artificial Intelligence and Cognitive Load Lab, Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland（人工智能与认知负荷实验室，应用智能研究中心，计算机科学学院，都柏林技术大学，都柏林，爱尔兰）
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）
Mild Traumatic Brain Injury (mTBI) is a common brain injury and affects a diverse group of people: soldiers, constructors, athletes, drivers, children, elders, and nearly everyone. Thus, having a well-established, fast, cheap, and accurate classification method is crucial for the well-being of people around the globe. Luckily, using Machine Learning (ML) on electroencephalography (EEG) data shows promising results. This survey analyzed the most cutting-edge articles from 2017 to the present. The articles were searched from the Google Scholar database and went through an elimination process bas…
Identifying seizure activities in non-stationary electroencephalography (EEG) is a challenging task, since it is time-consuming, burdensome, and dependent on expensive human resources and subject to error and bias. A computerized seizure identification scheme can eradicate the above problems, assist clinicians and benefit epilepsy research. So far, several attempts were made to develop automatic systems to help neurophysiologists accurately identify epileptic seizures. In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. T…
A Statistical Approach for Synthetic EEG Data Generation
College of Science and Engineering, James Cook University（科学与工程学院，詹姆斯库克大学）；College of Health Care Sciences, James Cook University（健康护理科学学院，詹姆斯库克大学）；College of Public Health, Medical, and Vet Sciences, James Cook University（公共卫生、医学与兽医学学院，詹姆斯库克大学）
The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy Detection
Computer Science Department, Federal University of Ouro Preto（欧鲁普雷图联邦大学计算机科学系）
