Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Frontiers in Neurorobotics, Год журнала: 2020, Номер 14

Опубликована: Июнь 3, 2020

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that application BCI not limited to medical applications, and hence, research this field has gained due attention. Moreover, significant number related publications over past two decades further indicates consistent improvements breakthroughs have been made particular field. Nonetheless, it also mentioning with these improvements, new challenges are constantly discovered. This article provides a comprehensive review state-of-the-art complete system. First, brief overview electroencephalogram (EEG)-based systems given. Secondly, considerable popular applications reviewed terms electrophysiological control signals, feature extraction, classification algorithms, performance evaluation metrics. Finally, recent discussed, possible solutions mitigate issues recommended.

Язык: Английский

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Deep learning for motor imagery EEG-based classification: A review

Biomedical Signal Processing and Control, Год журнала: 2020, Номер 63, С. 102172 - 102172

Опубликована: Окт. 8, 2020

Язык: Английский

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Brain functional and effective connectivity based on electroencephalography recordings: A review

Human Brain Mapping, Год журнала: 2021, Номер 43(2), С. 860 - 879

Опубликована: Окт. 20, 2021

Functional connectivity and effective of the human brain, representing statistical dependence directed information flow between cortical regions, significantly contribute to study intrinsic brain network its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing modeling estimating due increasing evidence that it can help better understand various neurological conditions. However, there is a lack comprehensive updated review EEG-based connectivity, particularly visualization options associated machine learning applications, aiming translate those techniques into useful clinical tools. This article reviews undertaken over last few years, in terms estimation, visualization, applications with classifiers. Methods are explored discussed from dimensions, such as either linear or nonlinear, parametric nonparametric, time-based, frequency-based time-frequency-based. Then followed by novel methods, grouped Heat Map, data statistics, Head explore variation across different regions. Finally, current challenges related research roadmap for future presented.

Язык: Английский

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A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images

Neurocomputing, Год журнала: 2022, Номер 481, С. 202 - 215

Опубликована: Янв. 21, 2022

Язык: Английский

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LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability

NeuroImage, Год журнала: 2023, Номер 276, С. 120209 - 120209

Опубликована: Июнь 2, 2023

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) pose a challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically, EEG-based recognition of activities states involves the use prior neuroscience knowledge generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract they often encounter issues such as poor generalization across datasets, high predicting volatility, model interpretability. To address these limitations, we propose novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two modules designed specifically signals, channel module depth module, LMDA-Net is able integrate features from multiple dimensions, resulting in improved classification performance various tasks. was evaluated on four high-impact public including motor imagery (MI) P300-Speller, compared with other representative models. The experimental results demonstrate that outperforms terms accuracy achieving highest all datasets within 300 training epochs. Ablation experiments further confirm effectiveness module. facilitate an in-depth understanding extracted by LMDA-Net, class-specific network feature interpretability algorithms are suitable evoked responses endogenous activities. mapping output specific layer time or domain through class activation maps, visualizations provide interpretable analysis establish connections time-spatial neuroscience. In summary, shows great potential general

Язык: Английский

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Cognitive neuroscience and robotics: Advancements and future research directions

Robotics and Computer-Integrated Manufacturing, Год журнала: 2023, Номер 85, С. 102610 - 102610

Опубликована: Июль 24, 2023

In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this conception, set a path convert neural activities recorded by sensors from the scalp via electroencephalography into valid commands for robot control and task execution. Thanks advancement sensor technologies, non-invasive invasive headsets designed developed achieve stable recording brainwave signals. However, robust accurate extraction interpretation signals robotics are critical reliable task-oriented opportunistic such brainwave-controlled robotic interactions. response need, pervasive advanced analytical approaches translating merging functions, behaviours, tasks, environmental information focus brain-controlled applications. These methods composed signal processing, feature extraction, representation activities, command conversion control. Artificial intelligence algorithms, deep learning, used classification, recognition, identification patterns intent underlying brainwaves form electroencephalography. Within context, paper provides comprehensive review past current status at intersection robotics, neuroscience, artificial highlights future research directions.

Язык: Английский

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A novel explainable machine learning approach for EEG-based brain-computer interface systems

Neural Computing and Applications, Год журнала: 2021, Номер 34(14), С. 11347 - 11360

Опубликована: Март 6, 2021

Язык: Английский

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Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

IEEE Transactions on Cognitive and Developmental Systems, Год журнала: 2021, Номер 14(2), С. 348 - 365

Опубликована: Май 13, 2021

Deep learning has achieved excellent performance in a wide range of domains, especially speech recognition and computer vision. Relatively less work been done for EEG, but there is still significant progress attained the last decade. Due to lack comprehensive topic widely covered survey deep we attempt summarize recent provide an overview, as well perspectives future developments. We first briefly mention artifacts removal EEG signal then introduce models that have utilized processing classification. Subsequently, applications are reviewed by categorizing them into groups such brain-computer interface, disease detection, emotion recognition. They followed discussion, which pros cons presented directions challenges proposed. hope this paper could serve summary past beginning further developments achievements studies based on learning.

Язык: Английский

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AutoEncoder Filter Bank Common Spatial Patterns to Decode Motor Imagery From EEG

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 27(5), С. 2365 - 2376

Опубликована: Фев. 9, 2023

The present paper introduces a novel method, named AutoEncoder-Filter Bank Common Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography (EEG). AE-FBCSP is an extension of the well-established FBCSP and based on global (cross-subject) subsequent transfer learning subject-specific (intra-subject) approach. A multi-way also introduced in this paper. Features are extracted high-density EEG (64 electrodes), by means FBCSP, used train custom AE, unsupervised way, project features into compressed latent space. Latent supervised classifier (feed forward neural network) movement. proposed method was tested using public dataset EEGs collected 109 subjects. consists right-hand, left-hand, both hands, feet motor imagery resting EEGs. extensively 3-way classification (right hand vs left resting) 2-way, 4-way 5-way ones, cross- intra-subject analysis. outperformed standard statistically significant way (p > 0.05) achieved average accuracy 89.09% classification. methodology performed better than other comparable methods literature, applied same dataset, tasks. One most interesting outcomes that remarkably increased number subjects responded with very high accuracy, which fundamental requirement for BCI systems be practice.

Язык: Английский

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Enhanced EEG-based Alzheimer’s disease detection using synchrosqueezing transform and deep transfer learning

Neuroscience, Год журнала: 2025, Номер 576, С. 105 - 117

Опубликована: Апрель 25, 2025

Язык: Английский

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