- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Growth of InGaAs solar cells on InP(001) miscut substrates using solid-source molecular beam epitaxy
摘要： In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
关键词： feature extraction,linear prediction,Brain-computer interface,orthogonal transform,channel selection
更新于2025-09-23 15:21:01
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Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface
摘要： Visual evoked potentials (VEPs) can be measured in the EEG as response to a visual stimulus. Commonly, VEPs are displayed by averaging multiple responses to a certain stimulus or a classifier is trained to identify the response to a certain stimulus. While the traditional approach is limited to a set of predefined stimulation patterns, we present a method that models the general process of VEP generation and thereby can be used to predict arbitrary visual stimulation patterns from EEG and predict how the brain responds to arbitrary stimulation patterns. We demonstrate how this method can be used to model single-flash VEPs, steady state VEPs (SSVEPs) or VEPs to complex stimulation patterns. It is further shown that this method can also be used for a high-speed BCI in an online scenario where it achieved an average information transfer rate (ITR) of 108.1 bit/min. Furthermore, in an off-line analysis, we show the flexibility of the method allowing to modulate a virtually unlimited amount of targets with any desired trial duration resulting in a theoretically possible ITR of more than 470 bit/min.
关键词： Visual evoked potentials,High-speed BCI,EEG,Information transfer rate,Brain-Computer Interface
更新于2025-09-23 15:21:01
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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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A Hybrid Evolutionary-Based MPPT for Photovoltaic Systems under Partial Shading Conditions
摘要： In their early days, brain–computer interfaces (BCIs) were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications has been substantially enlarged. Indeed, today BCI technology cannot only translate brain signals directly into control signals, but also can combine such kind of artificial output with a natural muscle-based output. Thus, the integration of multiple biological signals for real-time interaction holds the promise to enhance a much larger population than originally thought end users with preserved residual functions who could benefit from new generations of assistive technologies. A BCI system that combines a BCI with other physiological or technical signals is known as hybrid BCI (hBCI). In this work, we review the work of a large scale integrated project funded by the European commission which was dedicated to develop practical hybrid BCIs and introduce them in various fields of applications. This article presents an hBCI framework, which was used in studies with nonimpaired as well as end users with motor impairments.
关键词： communication,electroencephalogram,hybrid brain–computer interface (hBCI),Assistive technology,neuroprosthesis
更新于2025-09-23 15:19:57
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[IEEE 2018 11th Biomedical Engineering International Conference (BMEiCON) - Chiang Mai, Thailand (2018.11.21-2018.11.24)] 2018 11th Biomedical Engineering International Conference (BMEiCON) - Self-Flickering Visual Stimulus based on Visual illusion for SSVEP-based BCI System
摘要： This paper proposes the use of the windmill pattern visual stimulus to induce human vision by employing a phenomenon of visual illusion for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. We had to explore the brain response to the flickering pattern as windmill pattern, three BCI commands can be generated by using three different windmill patterns. SSVEP technique was used to detect the response. The average accuracy of classification was approximately 80.5%. With the proposed visual stimulus pattern, it can reduce eye fatigue and increase the number of commands for the existing SSVEP-based BCI. Therefore, the proposed visual stimulus pattern can be used for practical BCI applications
关键词： Steady-state visual evoked potential,Brain-computer interface,Visual illusion
更新于2025-09-19 17:15:36
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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 PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW) - London, United Kingdom (2019.6.18-2019.6.21)] 2019 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW) - Impacts of Coupling Plates on Single-Switch Capacitive-Coupled WPT Systems
摘要： In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
关键词： linear prediction,Brain-computer interface,channel selection,feature extraction,orthogonal transform
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