Non-invasive brain-computer interface technology has been developed for detecting human mental states with high performances. Detection of the pilots' mental states is particularly critical because their abnormal mental states could cause catastrophic accidents. In this study, we presented the feasibility of classifying distraction levels (namely, normal state, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attempt to classify distraction levels under a flight environment. We proposed a model for classifying distraction levels. A total of ten pilots conducted the experiment in a simulated flight environment. The grand-average accuracy was 0.8437 for classifying distraction levels across all subjects. Hence, we believe that it will contribute significantly to autonomous driving or flight based on artificial intelligence technology in the future.
Detecting driver distraction is a significant concern for future intelligent transportation systems. We present a new approach for identifying distracted driving behavior by evaluating a stimulus and response interaction with the brain signals in two ways. First, measuring the driver response through EEG by creating various types of distraction stimuli such as reading, texting, calling and using phone camera (risk odds ratio of these activities determined by NHTSA study). Second, using a survey, comparing driver's order/perception of severity of distraction with the derived distraction index f…
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Korea University（高丽大学）；University of Cambridge（剑桥大学）；Stanford University School of Medicine（斯坦福大学医学院）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗系统）；Korea University College of Medicine（高丽大学医学院）
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）
Learning aligned EEG representations with subject-specific encoders
University of São Paulo（圣保罗大学）；Université Paris-Saclay, Inria TAU team, LISN-CNRS（巴黎萨克雷大学，Inria TAU团队，LISN-CNRS）；Institut de neuromodulation, GHU Paris, psychiatrie et neurosciences, centre hospitalier Sainte-Anne, pôle hospitalo-universitaire 15, Université Paris Cité（神经调控研究所，GHU巴黎，精神病学与神经科学，圣安娜医院，大学医院中心15区，巴黎西岱大学）；Federal University of ABC (UFABC)（ABC联邦大学）；Yneuro；Swartz Center for Computational Neuroscience (SCCN), Institute for Neural Computation (INC), University of California San Diego（斯沃茨计算神经科学中心，神经计算研究所，加州大学圣地亚哥分校）
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）
Imperial College London（伦敦帝国学院）；Oxford Brookes University（牛津布鲁克斯大学）
The operating room (OR) is a dynamic and complex environment consisting of a multidisciplinary team working together in a high take environment to provide safe and efficient patient care. Additionally, surgeons are frequently exposed to multiple psycho-organisational stressors that may cause negative repercussions on their immediate technical performance and long-term health. Many factors can therefore contribute to increasing the Cognitive Workload (CWL) such as temporal pressures, unfamiliar anatomy or distractions in the OR. In this paper, a cascade of two machine learning approaches is sug…
TransformEEG: Towards Improving Model Generalizability in Deep Learning-based EEG Parkinson's Disease Detection
Department of Information Engineering, University of Padua（信息工程系，帕多瓦大学）；Department of Neuroscience, University of Padua（神经科学系，帕多瓦大学）；Padova Neuroscience Center, University of Padua（帕多瓦神经科学中心，帕多瓦大学）；Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais)（信息系统研究所，瑞士西部应用科学大学（HES-SO瓦莱））
Shenzhen University（深圳大学）；Kyoto University（京都大学）；Peng Cheng Laboratory（鹏城实验室）；ATR Neural Information Analysis Laboratories（ATR神经信息分析实验室）
How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG studies work in a task driven manner and explore the valid EEG features with a supervised model, which would be limited by the given labels to a great extent. In this paper, we propose a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature charact…
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）
Brain-computer interface (BCI) technologies have been widely used in many areas. In particular, non-invasive technologies such as electroencephalography (EEG) or near-infrared spectroscopy (NIRS) have been used to detect motor imagery, disease, or mental state. It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals. The fusion algorithm for EEG and NIRS sources is the key to implement them in real-life applications. In this research, we propose three fusion methods for the hybrid of the EEG and NIRS-based brain-comput…
Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four…
EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach
Nepal Engineering College（尼泊尔工程学院）；Pokhara University（博卡拉大学）；University of Delaware（特拉华大学）；Institute of Engineering Pulchowk Campus（工程学院普尔乔克校区）；Tribhuvan University（特里布文大学）；Nepal Research and Collaboration Center（尼泊尔研究与合作中心）
Malaviya National Institute of Technology（马拉维亚国家理工学院）；Maharaja Surajmal Institute of Technology（马哈拉贾·苏拉杰马尔理工学院）
In the field of neuroscience, Brain activity analysis is always considered as an important area. Schizophrenia(Sz) is a brain disorder that severely affects the thinking, behaviour, and feelings of people all around the world. Electroencephalography (EEG) is proved to be an efficient biomarker in Sz detection. EEG is a non-linear time-seriesi signal and utilizing it for investigation is rather crucial due to its non-linear structure. This paper aims to improve the performance of EEG based Sz detection using a deep learning approach. A novel hybrid deep learning model known as SzHNN (Schizophre…
EEG-Based Imagined Speech Decoding Using a Hybrid CNN-SNN Architecture
Doctoral School of Science, Technology, and Engineering, University of Granada（格拉纳达大学科学、技术与工程博士学院）；University of Technology Belfort-Montbéliard, SINERGIES（贝尔福-蒙贝利亚尔技术大学，协同实验室）；Department of Signal Theory, Telematics and Communications, University of Granada（格拉纳达大学信号理论、电信与通信系）；Department of Chemical Engineering, Stanford University（斯坦福大学化学工程系）；Department of Electrical and Computer Engineering, Aarhus University（奥尔胡斯大学电气与计算机工程系）
Intention decoding is an indispensable procedure in hands-free human-computer interaction (HCI). Conventional eye-tracking system using single-model fixation duration possibly issues commands ignoring users' real expectation. In the current study, an eye-brain hybrid brain-computer interface (BCI) interaction system was introduced for intention detection through fusion of multi-modal eye-track and ERP (a measurement derived from EEG) features. Eye-track and EEG data were recorded from 64 healthy participants as they performed a 40-min customized free search task of a fixed target icon among 25…
We proposed a novel visual stimulus for brain-computer interface. The stimulus is in the form gaiting sequence of a human. The hypothesis is that observing such a visual stimulus would simultaneously induce 1) steady-state motion visual evoked potential (SSMVEP) in the occipital area, similarly to an SSVEP stimulus; and 2) sensorimotor rhythm (SMR) in the primary sensorimotor area, because such action observation (AO) could activate the mirror neuron system. Canonical correlation analysis (CCA) was used to detect SSMVEP from occipital EEG, and event-related spectral perturbations (ERSP) were u…
Indian Institute of Information Technology Allahabad（印度阿拉哈巴德信息技术学院）
Emotion recognition using EEG signals is an emerging area of research due to its broad applicability in BCI. Emotional feelings are hard to stimulate in the lab. Emotions do not last long, yet they need enough context to be perceived and felt. However, most EEG-related emotion databases either suffer from emotionally irrelevant details (due to prolonged duration stimulus) or have minimal context doubting the feeling of any emotion using the stimulus. We tried to reduce the impact of this trade-off by designing an experiment in which participants are free to report their emotional feelings simu…
MHANet: Multi-scale Hybrid Attention Network for Auditory Attention Detection
Anhui Province Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）；Anhui University（安徽大学）
In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI) is a device that enables the use of the brain neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our …
Brain-computer interface (BCI) uses brain signals to communicate with external devices without actual control. Particularly, BCI is one of the interfaces for controlling the robotic arm. In this study, we propose a knowledge distillation-based framework to manipulate robotic arm through hybrid paradigm induced EEG signals for practical use. The teacher model is designed to decode input data hierarchically and transfer knowledge to student model. To this end, soft labels and distillation loss functions are applied to the student model training. According to experimental results, student model a…
