- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Optimal configuration of hybrid-energy microgrid considering the correlation and randomness of the wind power and photovoltaic power
摘要： Despite rapid advances in the study of brain–computer interfaces (BCIs) in recent decades, two fundamental challenges, namely, improvement of target detection performance and multidimensional control, continue to be major barriers for further development and applications. In this paper, we review the recent progress in multimodal BCIs (also called hybrid BCIs), which may provide potential solutions for addressing these challenges. In particular, improved target detection can be achieved by developing multimodal BCIs that utilize multiple brain patterns, multimodal signals, or multisensory stimuli. Furthermore, multidimensional object control can be accomplished by generating multiple control signals from different brain patterns or signal modalities. Here, we highlight several representative multimodal BCI systems by analyzing their paradigm designs, detection/control methods, and experimental results. To demonstrate their practicality, we report several initial clinical applications of these multimodal BCI systems, including awareness evaluation/detection in patients with disorder of consciousness (DOC). As an evolving research area, the study of multimodal BCIs is increasingly requiring more synergetic efforts from multiple disciplines for the exploration of the underlying brain mechanisms, the design of new effective paradigms and means of neurofeedback, and the expansion of the clinical applications of these systems.
关键词： brain switch,multimodal brain–computer interface (BCI),awareness evaluation,Audiovisual BCI,cursor control
更新于2025-09-23 15:19:57
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[IEEE 2018 International Conference on Frontiers of Information Technology (FIT) - Islamabad, Pakistan (2018.12.17-2018.12.19)] 2018 International Conference on Frontiers of Information Technology (FIT) - Mesh of SSVEP-Based BCI and Eye-Tracker for Use of Higher Frequency Stimuli and Lower Number of EEG Channels
摘要： Steady-State Visually Evoked Potential (SSVEP) is widely used in brain-computer interface (BCI) systems. However, the use of flickering stimuli at low frequencies causes visual fatigue for users. The visual fatigue increases when multiple stimuli are used, flickering at different frequencies. To overcome this problem, this paper present a solution by using single high frequency (30 Hz) stimulus interface with 30 targets. In the proposed system, the initial recognition of the target was achieved through the eye gaze position using an eye-tracker, and the selection/classification of command was provided by EEG. As only a single stimulating frequency was used (i.e. 30 Hz), thus, only two EEG electrodes (at positions PZ and OZ ) were used along with g.USBamp amplifier. This reduced the setup-time for the preparation of the users. A new calibration technique for the eye tracker was designed and developed, which resulted in better eye gaze tracking. The results showed that higher classification accuracies can be achieved by using the mesh of SSVEP-based BCI system and eye-tracker as compared to the SSVEP-based BCI system.
关键词： The EyeTribe calibration,High frequency stimuli,Brain-Computer Interface(BCI),Steady-State Visual Evoked Potential (SSVEP)
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE 21st Electronics Packaging Technology Conference (EPTC) - Singapore, Singapore (2019.12.4-2019.12.6)] 2019 IEEE 21st Electronics Packaging Technology Conference (EPTC) - Fabrication of High Voltage Capable TSV Using Backside via Last Process and Laser Abblation of Dry Film BCB
摘要： Despite rapid advances in the study of brain–computer interfaces (BCIs) in recent decades, two fundamental challenges, namely, improvement of target detection performance and multidimensional control, continue to be major barriers for further development and applications. In this paper, we review the recent progress in multimodal BCIs (also called hybrid BCIs), which may provide potential solutions for addressing these challenges. In particular, improved target detection can be achieved by developing multimodal BCIs that utilize multiple brain patterns, multimodal signals, or multisensory stimuli. Furthermore, multidimensional object control can be accomplished by generating multiple control signals from different brain patterns or signal modalities. Here, we highlight several representative multimodal BCI systems by analyzing their paradigm designs, detection/control methods, and experimental results. To demonstrate their practicality, we report several initial clinical applications of these multimodal BCI systems, including awareness evaluation/detection in patients with disorder of consciousness (DOC). As an evolving research area, the study of multimodal BCIs is increasingly requiring more synergetic efforts from multiple disciplines for the exploration of the underlying brain mechanisms, the design of new effective paradigms and means of neurofeedback, and the expansion of the clinical applications of these systems.
关键词： multimodal brain–computer interface (BCI),awareness evaluation,Audiovisual BCI,brain switch,cursor control
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Contact Resistivity and Sheet Resistance Measurements of Cells Extracted from Field-aged Modules
摘要： Despite rapid advances in the study of brain–computer interfaces (BCIs) in recent decades, two fundamental challenges, namely, improvement of target detection performance and multidimensional control, continue to be major barriers for further development and applications. In this paper, we review the recent progress in multimodal BCIs (also called hybrid BCIs), which may provide potential solutions for addressing these challenges. In particular, improved target detection can be achieved by developing multimodal BCIs that utilize multiple brain patterns, multimodal signals, or multisensory stimuli. Furthermore, multidimensional object control can be accomplished by generating multiple control signals from different brain patterns or signal modalities. Here, we highlight several representative multimodal BCI systems by analyzing their paradigm designs, detection/control methods, and experimental results. To demonstrate their practicality, we report several initial clinical applications of these multimodal BCI systems, including awareness evaluation/detection in patients with disorder of consciousness (DOC). As an evolving research area, the study of multimodal BCIs is increasingly requiring more synergetic efforts from multiple disciplines for the exploration of the underlying brain mechanisms, the design of new effective paradigms and means of neurofeedback, and the expansion of the clinical applications of these systems.
关键词： brain switch,multimodal brain–computer interface (BCI),awareness evaluation,Audiovisual BCI,cursor control
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Ways of Producing Perovskite Light Absorbing Layer on Periodically Patterned Silicon Texture and Evaluating Method
摘要： A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed of?ine on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.
关键词： independent component analysis,wavelets,brain-computer interface (BCI),electroencephalogram (EEG),Automated online artifact removal
更新于2025-09-19 17:13:59
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Expression and significance of quantum dots in RAW 264.7 macrophages
摘要： Robotic co-workers are an emerging generation of physical robots promises to transform manufacturing with its ability to communicate and collaborate, both robot-to-robot and robot-to-human, opening the way to greater innovation and productivity. We designed a welding robotic co-worker which observes industrial part, searches welding seams, plans welding trajectory, and simulates welding result automatically. It largely benefits small and medium-sized manufacturing enterprises (SMEs) by allowing manufacturers handle low-volume orders without re-programming. As a co-worker, the robot communicates with its operator though brain computer interface (BCI) as well as guarantees the operator’s safety. We demonstrated the performance of this robot using a human subject study in welding simulation environment.
关键词： Safety,Virtual welding simulator,Brain Computer Interface (BCI),Small and medium-sized manufacturing enterprises (SMEs),Welding robotic co-worker
更新于2025-09-11 14:15:04
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - A Modified Common Spatial Pattern Algorithm Customized for Feature Dimensionality Reduction in fNIRS-Based BCIs
摘要： Functional near-infrared spectroscopy (fNIRS) is a non-invasive multi-channel imaging tool for assessing brain activities, which has shown its high potential in brain-computer interface (BCI) technique. Most previous studies have focused on constructing high dimensional features from whole channels, adding to the complexity of their classifiers. Another multi-channel source for BCI is electroencephalograph (EEG), which possesses different spatial and temporal features from fNIRS. In EEG field, Common Spatial Pattern (CSP) algorithm is widely used aimed at dimensionality reduction. In our article, we modified it based on the characteristics of fNIRS and evaluated its effectiveness in discriminating Mental Arithmetic (MA) against resting status in an open-access dataset. The Modified Common Spatial Pattern algorithm significantly outperforms CSP algorithm in fNIRS-based BCI and shows its potential in further BCI related explorations.
关键词： Common Spatial Pattern (CSP),Mental Arithmetic (MA),Modified Common Spatial Pattern (MCSP),brain-computer interface (BCI),Functional near-infrared spectroscopy (fNIRS)
更新于2025-09-10 09:29:36