检索结果-内蒙古大学图书馆

Designing brain-computer interfaces for Attention-Aware Systems

computer 2015年第10期48卷 34-+页

作者： Peck, Evan M. Carlin, Emily Jacob, Robert Bucknell Univ Comp Sci Lewisburg PA 17837 USA Tufts Univ Comp Sci Medford MA USA Tufts Univ Cognit & Brain Sci Medford MA USA

brain-computer interfaces (BCIs) hold great promise for improving information delivery and preserving user attention, but this promise has not yet translated to practical use. A prototype BCI that optimizes email notifications in noisy, complex environments, CARSON combines multiple measures from the brain to predict both cognitive workload and message relevancy to determine the optimum time to interrupt the user.

关键词： brain-computer interfaces cognition electronic mail BCI CARSON attention-aware systems brain-computer interfaces cognitive workload email notifications information delivery message relevancy user attention Biomedical monitoring brain models brain-computer interfaces Electronic mail Noise measurement Physiological computing Sensors BCIs CARSON Cognitive Automatic Real-Time Selection of Notifications HCI attention-aware systems brain-computer interfaces email fNIRS functional near-infrared spectroscopy human-computer interaction intelligent systems physiological computing

来源：评论

学校读者我要写书评

暂无评论

A genetic interval type-2 fuzzy logic-based approach for generating interpretable linguistic models for the brain P300 phenomena recorded via brain-computer interfaces

引用

SOFT COMPUTING 2015年第4期19卷 1019-1035页

作者： Alhaddad, Mohammed J. Mohammed, Ahmed Kamel, Mahmoud Hagras, Hani King Abdulaziz Univ Jeddah 21413 Saudi Arabia Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

One of the important areas of brain-computer interface (BCI) research is to identify event-related potentials (ERPs) which are spatial-temporal patterns of the brain activity that happen after presentation of a stimulus and before execution of a movement. One of the important ERPs is the P300 which is an endogenous component of ERPs with a latency of about 300 ms which is elicited by significant stimuli (visual, or auditory). Various machine learning-based classifiers have been used to predict the P300 events and relate them to the human intended activities. However, the vast majority of the employed techniques like Bayesian linear discriminant analysis (BLDA) and regularized fisher linear discriminant analysis (RFLDA) are black box models which are difficult to understand and analyse by a normal clinician. In addition, due to the inter- and intra-user uncertainties associated with the P300 events, most of the existing classifiers need to be trained for a specific user under specific circumstances and the classifier needs to be retrained for different users or change of circumstances. In this paper, we present an interval type-2 fuzzy logic-based classifier which is able to handle the users' uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximise the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. We will present various experiments which were performed on standard data sets and using real-data sets obtained from real subjects' experiments performed in the BCI laboratory in King Abdulaziz University. It wi

关键词： Type-2 fuzzy logic systems Linguistic model generation Modeling perceptions brain-computer interfaces

来源：评论

学校读者我要写书评

暂无评论

Statistical Characterization of Steady-State Visual Evoked Potentials and Their Use in brain-computer interfaces

引用

NEURAL PROCESSING LETTERS 2009年第3期29卷 179-187页

作者： Lopez, Miguel A. Pelayo, Francisco Madrid, Eduardo Prieto, Alberto Univ Granada Dept Signal Theory Telemat & Commun Granada Spain Univ Granada Dept Architecture & Technol Comp Granada Spain Univ Granada Fac Psychol Granada Spain

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) use the spectral power of the potentials for classification as they can be voluntarily enhanced or diminished by the subject by means of selective attention. The features traditionally extracted from the EEG and used for BCIs have been characterized as a normal distribution, although some studies have shown recently that this normal distribution is not the most appropriate for SSVEPs. In this paper we attempt to characterize the power of SSVEPs as a random variable that follows Rayleigh and exponential distributions when the stimulus is attended and ignored, respectively. BCIs based on SSVEPs can improve the transfer-bit and successful-classification rates if this new model is used instead of the traditional one based on the normal distribution.

关键词： brain-computer interfaces Rayleigh distribution Exponential distribution Steady-state visual evoked potentials Selective attention EEG Classification

来源：评论

学校读者我要写书评

暂无评论

Employment of Domain Adaptation Techniques in SSVEP-Based brain-computer interfaces

引用

IEEE ACCESS 2023年 11卷 36147-36157页

作者： Apicella, Andrea Arpaia, Pasquale De Benedetto, Egidio Donato, Nicola Duraccio, Luigi Giugliano, Salvatore Prevete, Roberto Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy Univ Naples Federico II Interdept Res Ctr Hlth Management & Innovat Health I-80125 Naples Italy Univ Messina Dept Engn I-98122 Messina Italy Polytech Univ Turin Dept Elect & Telecommun I-10129 Turin Italy

This work addresses the employment of Machine Learning (ML) and Domain Adaptation (DA) in the framework of brain-computer interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs). Currently, all the state-of-the-art classification strategies do not consider the high non-stationarity typical of brain signals. This can lead to poor performance, expecially when short-time signals have to be considered to allow real-time human-environment interaction. In this regard, ML and DA techniques can represent a suitable strategy to enhance the performance of SSVEPs classification pipelines. In particular, the employment of a two-step DA technique is proposed: first, the standardization of the data per subject is performed by exploiting a part of unlabeled test data during the training stage;second, a similarity measure between subjects is considered in the selection of the validation sets. The proposal was applied to three classifiers to verify the statistical significance of the improvements over the standard approaches. These classifiers were validated and comparatively tested on a well-known public benchmark dataset. An appropriate validation method was used in order to simulate real-world usage. The experimental results show that the proposed approach significantly improves the classification accuracy of SSVEPs. In fact, up to 62.27 % accuracy was achieved also in the case of short-time signals (i.e., 1.0 s). This represents a further confirmation of the suitability of advanced ML to improve the performance of BCIs for daily-life applications.

关键词： brain-computer interfaces Electroencephalography brain modeling Data models Training data Real-time systems Biomedical measurements Neural engineering brain--computer interface domain adaptation EEG EEGNet health 4.0 instrumentation machine learning neural engineering neural networks SSVEP real-time systems

来源：评论

学校读者我要写书评

暂无评论

Single-Trial NIRS Data Classification for brain-computer interfaces Using Graph Signal Processing

引用

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2018年第9期26卷 1700-1709页

作者： Petrantonakis, Panagiotis C. Kompatsiaris, Ioannis Ctr Res & Technol Hellas Inst Informat Technol Thessaloniki 57001 Greece

Near-infrared spectroscopy (NIRS)-based brain-computer interface (BCI) systems use feature extraction methods relying mainly on the slope characteristics and mean changes of the hemodynamic responses in respect to certain mental tasks. Nevertheless, spatial patterns across the measurement channels have been detected and should be considered during the feature vector extraction stage of the BCI realization. In this paper, a graph signal processing (GSP) approach for feature extraction is adopted in order to capture the aforementioned spatial information of the NIRS signals. The proposed GSP-based methodology for feature extraction in NIRS-based BCI systems, namely graph NIRS (GNIRS), is applied on a publicly available dataset of NIRS recordings during a mental arithmetic task. GNIRS exhibits higher classification rates (CRs), up to 92.52%, as compared to the CRs of two state-of-the-art feature extraction methodologies related to slope and mean values of hemodynamic response, i.e., 90.35% and 82.60%, respectively. In addition, GNIRS leads to the formation of feature vectors with reduced dimensionality in comparison with the baseline approaches. Moreover, it is shown to facilitate high CRs even from the first second after the onset of the mental task, paving the way for faster NIRS-based BCI systems.

关键词： brain-computer interfaces graph signal processing mental arithmetic near-infrared spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Human Neuro-Activity for Securing Body Area Networks: Application of brain-computer interfaces to People-Centric Internet of Things

引用

IEEE COMMUNICATIONS MAGAZINE 2017年第2期55卷 62-67页

作者： Valenzuela-Valdes, Juan F. Angel Lopez, Miguel Padilla, Pablo Padilla, Jose L. Minguillon, Jesus Univ Granada Granada Spain Univ Granada Dept Signal Theory Telemat & Commun Granada Spain Univ Granada Marie Curie Programme Granada Spain Univ Granada Dept Comp Architecture & Technol Granada Spain Ecole Polytech Fed Lausanne Nanoelect Devices Lab Lausanne Switzerland

A former definition states that a brain corn puter Interface provides a direct communication channel to the brain without the need for muscles and nerves. With the emergence of wearable and wireless brain-computer interlaces, these systems have evolved to become part of wireless body area networks, offering people-centric applications such as cognitive workload assessment and detection of selective attention. Currently, wireless body area networks are mostly integrated by low-cost devices that, because of their limited hardware resources, cannot generate secure random numbers for encryption. This is a critical issue in the context of new Internet of Things device communication and its security. Such devices require securing their communication, mostly by means of the automatic renewal of the cryptographic keys. In the domain of the people-centric Internet of Things, we propose to use wireless brain-computer interfaces as a secure source of entropy, based on neuro-activity, capable to generate secure keys that outperforms other generation methods. In our approach, current wireless brain-computer interface technology is an attractive option to offer novel services emerged from novel necessities in the context of the people-centric Internet of Things. Our proposal is an implementation of the human-in-the-loop paradigm, in which devices and humans indistinctly request and offer services to each other for mutual benefit.

关键词： brain-computer interfaces MUSCLES NERVES BODY area networks (Electronics) INTERNET of things

来源：评论

学校读者我要写书评

暂无评论

Developing brain-computer interfaces from a user-centered perspective: Assessing the needs of persons with amyotrophic lateral sclerosis, caregivers, and professionals

引用

APPLIED ERGONOMICS 2015年 50卷 139-146页

作者： Liberati, Giulia Pizzimenti, Alessia Simione, Luca Riccio, Angela Schettini, Francesca Inghilleri, Maurizio Mattia, Donatella Cincotti, Febo Fdn Santa Lucia Rome Italy Catholic Univ Louvain Inst Neurosci B-1200 Brussels Belgium Crossing Dialogues Assoc Rome Italy Univ Roma La Sapienza Dept Neurol & Psychiat ALS Ctr Rome Italy CNR Inst Cognit Sci & Technol Rome Italy Univ Roma La Sapienza Dept Psychol Rome Italy Univ Roma La Sapienza DIAG Rome Italy

By focus group methodology, we examined the opinions and requirements of persons with ALS, their caregivers, and health care assistants with regard to developing a brain-computer interface (BCI) system that fulfills the user's needs. Four overarching topics emerged from this analysis: 1) lack of information on BCI and its everyday applications;2) importance of a customizable system that supports individuals throughout the various stages of the disease;3) relationship between affectivity and technology use;and 4) importance of individuals retaining a sense of agency. These findings should be considered when developing new assistive technology. Moreover, the BCI community should acknowledge the need to bridge experimental results and its everyday application. (C) 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

关键词： Amyotrophic lateral sclerosis brain-computer interfaces User-centered approach

来源：评论

学校读者我要写书评

暂无评论

A Feedback Information-Theoretic Approach to the Design of brain-computer interfaces

引用

INTERNATIONAL JOURNAL OF HUMAN-computer INTERACTION 2011年第1期27卷 5-23页

作者： Omar, Cyrus Akce, Abdullah Johnson, Miles Bretl, Timothy Ma, Rui Maclin, Edward McCormick, Martin Coleman, Todd P. Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA

This article presents a new approach to designing brain-computer interfaces (BCIs) that explicitly accounts for both the uncertainty of neural signals and the important role of sensory feedback. This approach views a BCI as the means by which users communicate intent to an external device and models intent as a string in an ordered symbolic language. This abstraction allows the problem of designing a BCI to be reformulated as the problem of designing a reliable communication protocol using tools from feedback information theory. Here, this protocol is given by a posterior matching scheme. This scheme is not only provably optimal but also easily understood and implemented by a human user. Experimental validation is provided by an interface for text entry and an interface for tracing smooth planar curves, where input is taken in each case from an electroencephalograph during left-and right-hand motor imagery.

关键词： COMMUNICATION computer network protocols USER interfaces (computer systems) brain-computer interfaces ALCOM (computer program language)

来源：评论

学校读者我要写书评

暂无评论

Classification of brain hemodynamic signals arising from visual action observation tasks for brain-computer interfaces: A functional near-infrared spectroscopy study

引用

MEASUREMENT 2014年第1期49卷 320-328页

作者： Abibullaev, Berdakh An, Jinung Jin, Sang-Hyeon Moon, Jeon Il Daegu Gyeongbuk Inst Sci & Technol Taegu 711873 South Korea

A brain-computer interface (BCI) is a way of translating an individuals' thoughts to control a computer or an external mechanical device. Studying brain activities in a reproducible manner, this study explores the possibility of using real-time functional-near infrared spectroscopy (fNIRS) to detect brain hemodynamic features for BCI commands. Sixteen channel brain activities associated with two distinct mental tasks were measured from seven healthy subjects. The tasks represented neural activities arising from a visual observation of a motor action related to hand movements of the subjects. Sensitive signatures of task relevant neural activities were further extracted from hemodynamic signals in the prefrontal cortex of the brain, and subsequently were translated into pre-determined computer commands using a set of algorithms. The decoded commands allowed volunteer subjects to control an external device in real-time through their mental intentions. The obtained results demonstrate the potential of the current study as an alternative fNIRS-BCI paradigm. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Functional near-infrared spectroscopy brain-computer interfaces Action observation tasks SVM PCA brain hemodynamic signals

来源：评论

学校读者我要写书评

暂无评论

A Flexible, Robust, and Gel-Free Electroencephalogram Electrode for Noninvasive brain-computer interfaces

引用

NANO LETTERS 2019年第10期19卷 6853-6861页

作者： Lin, Sen Liu, Junchen Li, Wenzheng Wang, Dong Huang, Ya Jia, Chao Li, Ziwei Murtaza, Muhammad Wang, Haiyang Song, Jianan Liu, Zhenglian Huang, Kai Zu, Di Lei, Ming Hong, Bo Wu, Hui Tsinghua Univ Sch Mat Sci & Engn State Key Lab New Ceram & Fine Proc Beijing 100084 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Tsinghua Univ Sch Med Dept Biomed Engn Beijing 100084 Peoples R China

brain-computer interfaces (BCIs) enable direct and near-instant communication between the brain and electronic devices. One of the biggest remaining challenges is to develop an effective noninvasive BCI that allows the recording electrodes to avoid hair on human skin without the inconveniences and complications of using a conductive gel. In this study, we developed a cost-effective, easily manufacturable, flexible, robust, and gel-free silver nanowire/polyvinyl butyral (PVB)/melamine sponge (AgPMS) electroencephalogram (EEG) electrode that circumvents problems with hair. Because of surface metallization by the silver nanowires (AgNWs), the sponge has a high conductivity of 917 S/m while its weight remains the same. The flexible sponge framework and self-locking AgNWs combine to give the new electrode remarkable mechanical stability (the conductivity remains unchanged after 10 000 cycles at 10% compression) and the ability to bypass hair. A BCI application based on steady-state visual evoked potential (SSVEP) measurements on hairless skin shows that the BCI accuracy of the new electrode (86%) is approximately the same as that of conventional electrodes supported by a conductive gel (88%). Most importantly, the performance of the AgPMS on hairy skin is not significantly reduced, which indicates that the new electrode can replace conventional electrodes for both hairless and hairy skin BCIs and other EEG applications.

关键词： brain-computer interfaces flexible electronics silver nanowire gel-free electrode

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：