作者：
Si, XiaopengHuang, HeYu, JiayueMing, DongTianjin University
Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Institute of Applied Psychology Tianjin300072 China Tianjin University
Academy of Medical Engineering and Translational Medicine State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin300072 China
The affective brain-computer interface (aBCI) facilitates the objective identification or regulation of human emotions. Current aBCI mainly relies on electroencephalography (EEG). However, research shows that emotions...
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作者：
Arghand, RezaChaibakhsh, AliRadman, MoeinUniversity of Guilan
Intelligent Systems and Advanced Control Lab Faculty of Mechanical Engineering Rasht Guilan41996-13776 Iran University of Essex
Brain-Computer Interfacing and Neural Engineering Laboratory School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom
brain-computer Interface (BCI) systems are relatively new technologies that could play a significant role in aiding the recovery of impaired activities resulting from neuromuscular disabilities in affected individuals...
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Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of ef...
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Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.
Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explore...
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With the gradual realization of manned spaceflight goals, the physical and mental health of astronauts has become a core concern. Numerous studies in recent years have indicated that the aerospace special environment ...
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Objective: Low intensity focused stimulation (LIFUS) has been proved to improve motor function in Parkinson's disease (PD) animal modules. The aim of this study is to investigate whether LIFUS target on the primar...
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Exploring new effective treatments for depression holds important social significance and clinical value. Low-intensity focused ultrasound stimulation (LIFUS) has been proven to have significant neuroprotective effect...
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brain-computer Interface (BCI) systems are relatively new technologies that could play a significant role in aiding the recovery of impaired activities resulting from neuromuscular disabilities in affected individuals...
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ISBN:
(数字)9798331511272
ISBN:
(纸本)9798331511289
brain-computer Interface (BCI) systems are relatively new technologies that could play a significant role in aiding the recovery of impaired activities resulting from neuromuscular disabilities in affected individuals. Accurate recognition and classification of motor imagery in BCI systems present a challenge, leading to extensive research in recent years aimed at improving the accuracy of these systems. In this study, a combination of the Empirical Mode Decomposition (EMD) method and a multi-layer Convolutional neural Network (CNN) is employed. Initially, the signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and all IMFs across all trials are analyzed to select the best ones, which are then fed into the CNN as inputs. Additionally, the study incorporates the fusion of three CNN networks, each corresponding to a different IMF. The features extracted from these networks are combined and used to train an SVM classifier. The proposed method achieved an accuracy of 86.1% on the BCI-2a 2008 dataset, outperforming other state-of-the-art approaches.
Surface electromyography (sEMG) signals-based gesture recognition method is widely employed in human-computer interaction task. In this paper, we proposed a phase locked value (PLV)-based feature extraction method for...
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The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much atten...
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