Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography (EEG) signals are distorted by movement artifacts and electromyography signals when users are moving, 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 has been widely used. In this paper, we proposed ensemble-based convolutional neural networks in ambulatory environment and analyzed the visual event-related potential responses in scalp- and ear-EEG in terms of statistical analysis and brain-computer interface performance. The brain-computer interface performance deteriorated as 3-14% when walking fast at 1.6 m/s. The proposed methods showed 0.728 in average of the area under the curve. The proposed method shows robust to the ambulatory environment and imbalanced data as well.
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…
Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals
School of Electrical and Electronic Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；School of Information Technology, Murdoch University（莫道克大学信息技术学院）；Daffodil International University（水仙国际大学）
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）
IEFS-GMB: Gradient Memory Bank-Guided Feature Selection Based on Information Entropy for EEG Classification of Neurological Disorders
Institute of Artificial Intelligence, Heifei Comprehensive National Science Center（人工智能研究院）；School of Artificial Intelligence, Anhui University（人工智能学院）；McGovern Institute for Brain Research, Peking University（脑科学研究院）；Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University（磁共振研究中心）；Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University（多模态认知计算重点实验室）
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））
BEAM: Brainwave Empathy Assessment Model for Early Childhood
ShanghaiTech University（上海科技大学）；East China Normal University（华东师范大学）；Children’s Hospital of Fudan University (Xiamen Branch)（复旦大学附属儿童医院（厦门分院））；State Key Laboratory of Advanced Medical Materials and Devices（国家先进医疗材料与设备重点实验室）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
The application of psychophysiology in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of an array of physiological signals and analysis techniques. Methods to study central nervous system (CNS) are usually expensive and laborious. However, electroencephalography (EEG) is one of the most affordable and ambulatory methodologies for CNS research. It is in use in various clinical studies and have been broadly studied over decades. Despite that the recorded EEG signals are quite pr…
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…
Observing brain activity in real-world settings offers exciting possibilities like the support of physical health, mental well-being, and thought-controlled interaction modalities. The development of such applications is, however, strongly impeded by poor accessibility to research-grade neural data and by a lack of easy-to-use and comfortable sensors. This work presents the cost-effective adaptation of concealed around-the-ear EEG electrodes (cEEGrids) to the open-source OpenBCI EEG signal acquisition platform to provide a promising new toolkit. An integrated system design is described, that c…
Transformers are groundbreaking architectures that have changed a flow of deep learning, and many high-performance models are developing based on transformer architectures. Transformers implemented only with attention with encoder-decoder structure following seq2seq without using RNN, but had better performance than RNN. Herein, we investigate the decoding technique for electroencephalography (EEG) composed of self-attention module from transformer architecture during imagined speech and overt speech. We performed classification of nine subjects using convolutional neural network based on EEGN…
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）
We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-ev…
Eriksholm Research Centre（埃里克肖尔姆研究中心）；Linköping University（林雪平大学）；University of Stuttgart（斯图加特大学）；Technical University of Denmark（丹麦技术大学）；Oticon A/S（奥迪康公司）
Attending to the speech stream of interest in multi-talker environments can be a challenging task, particularly for listeners with hearing impairment. Research suggests that neural responses assessed with electroencephalography (EEG) are modulated by listener`s auditory attention, revealing selective neural tracking (NT) of the attended speech. NT methods mostly rely on hand-engineered acoustic and linguistic speech features to predict the neural response. Only recently, deep neural network (DNN) models without specific linguistic information have been used to extract speech features for NT, d…
Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Department of Computer Science, Imam Mohammad Ibn Saud Islamic University（伊玛姆·莫哈默德·伊本·沙特伊斯兰大学计算机科学系）；Department of Computer Science, Princess Nourah bint Abdulrahman University（努拉·本·阿卜杜勒拉赫曼大学计算机科学系）
Drowsiness, which is the state when drivers do not have scheduled breaks while traveling long distances, is the main reason behind serious motorway accidents. Accordingly, experts claim that drowsy state is hard to be recognized early enough to prevent serious accidents that may lead even to road deaths. In this work, we propose a new drowsiness state detection system based on physiological signals and eye blinking. An experiment has been directed to justify the utility of the proposed approach. This system uses a smart video camera that takes drivers faces images and supervises the eye blink …
Technology advancements made it easy to measure non-invasive and high-quality electroencephalograph (EEG) signals from human's brain. Hence, development of robust and high-performance AI algorithms becomes crucial to properly process the EEG signals and recognize the patterns, which lead to an appropriate control signal. Despite the advancements in processing the motor imagery EEG signals, the healthcare applications, such as emotion detection, are still in the early stages of AI design. In this paper, we propose a modular framework for the recognition of vowels as the AI part of a brain compu…
