The Experts below are selected from a list of 48636 Experts worldwide ranked by ideXlab platform
Baoliang Lu - One of the best experts on this subject based on the ideXlab platform.
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a Multimodal Approach to estimating vigilance using eeg and forehead eog
Journal of Neural Engineering, 2017Co-Authors: Weilong Zheng, Baoliang LuAbstract:OBJECTIVE:Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estimating the vigilance of users using EEG and EOG signals. Approach:The PERCLOS index as vigilance annotation is obtained from eye tracking glasses. To improve the feasibility and wearability of vigilance estimation devices for real-world applications, we adopt a novel electrode placement for forehead EOG and extract various eye movement features, which contain the principal information of traditional EOG. We explore the effects of EEG from different brain areas and combine EEG and forehead EOG to leverage their complementary characteristics for vigilance estimation. Considering that the vigilance of users is a dynamic changing process because the intrinsic mental states of users involve temporal evolution, we introduce continuous conditional neural field and continuous conditional random field models to capture dynamic temporal dependency. MAIN RESULTS:We propose a Multimodal Approach to estimating vigilance by combining EEG and forehead EOG and incorporating the temporal dependency of vigilance into model training. The experimental results demonstrate that modality fusion can improve the performance compared with a single modality, EOG and EEG contain complementary information for vigilance estimation, and the temporal dependency-based models can enhance the performance of vigilance estimation. From the experimental results, we observe that theta and alpha frequency activities are increased, while gamma frequency activities are decreased in drowsy states in contrast to awake states. SIGNIFICANCE:The forehead setup allows for the simultaneous collection of EEG and EOG and achieves comparative performance using only four shared electrodes in comparison with the temporal and posterior sites.
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a Multimodal Approach to estimating vigilance using eeg and forehead eog
arXiv: Human-Computer Interaction, 2016Co-Authors: Weilong Zheng, Baoliang LuAbstract:Objective. Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estimating the vigilance of users using EEG and EOG signals. Approach. To improve the feasibility and wearability of vigilance estimation devices for real-world applications, we adopt a novel electrode placement for forehead EOG and extract various eye movement features, which contain the principal information of traditional EOG. We explore the effects of EEG from different brain areas and combine EEG and forehead EOG to leverage their complementary characteristics for vigilance estimation. Considering that the vigilance of users is a dynamic changing process because the intrinsic mental states of users involve temporal evolution, we introduce continuous conditional neural field and continuous conditional random field models to capture dynamic temporal dependency. Main results. We propose a Multimodal Approach to estimating vigilance by combining EEG and forehead EOG and incorporating the temporal dependency of vigilance into model training. The experimental results demonstrate that modality fusion can improve the performance compared with a single modality, EOG and EEG contain complementary information for vigilance estimation, and the temporal dependency-based models can enhance the performance of vigilance estimation. From the experimental results, we observe that theta and alpha frequency activities are increased, while gamma frequency activities are decreased in drowsy states in contrast to awake states. Significance. The forehead setup allows for the simultaneous collection of EEG and EOG and achieves comparative performance using only four shared electrodes in comparison with the temporal and posterior sites.
S. De Blok - One of the best experts on this subject based on the ideXlab platform.
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Multimodal Approach to postoperative pain control in daycase laparoscopic sterilization.
Anesthesia and analgesia, 1998Co-Authors: R. Van Ee, D. J. Hemrika, S. De BlokAbstract:This article presents the results of a Multimodal Approach to postoperative pain control in laparoscopic sterilization. In a clinical research study results indicated that for treating pain after laparoscopic sterilization nonsteroidal anti-inflammatory drugs (NSAID) alone are unlikely to provide adequate postoperative pain relief. Unsatisfactory effects of naproxen suppositories administered preoperatively were reported by most of the patients still requiring opioids. In addition ambulation and rehabilitation was usually prolonged. However in the Multimodal Approach excellent postoperative pain relief and facilitated rapid postoperative rehabilitation were noted in the evaluation of a perioperative pain protocol. This protocol combined a preoperative NSAID (ketoprofen) with direct intraoperative mesosalpinx infiltration. These conclusions correspond with one of the conclusions drawn by Chung et al. which states that certain types of surgery could lead to severe postoperative pain.
John R. Morris - One of the best experts on this subject based on the ideXlab platform.
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Correlated Multimodal Approach Reveals Key Details of Nerve-Agent Decomposition by Single-Site Zr-Based Polyoxometalates.
The Journal of Physical Chemistry Letters, 2019Co-Authors: Yiyao Tian, Anna M. Plonka, Amani M. Ebrahim, Robert M. Palomino, Sanjaya D. Senanayake, Alex Balboa, Wesley O. Gordon, Diego Troya, Djamaladdin G. Musaev, John R. MorrisAbstract:Development of technologies for protection against chemical warfare agents (CWAs) is critically important. Recently, polyoxometalates have attracted attention as potential catalysts for nerve-agent decomposition. Improvement of their effectiveness in real operating conditions requires an atomic-level understanding of CWA decomposition at the gas–solid interface. We investigated decomposition of the nerve agent Sarin and its simulant, dimethyl chlorophosphate (DMCP), by zirconium polytungstate. Using a Multimodal Approach, we showed that upon DMCP and Sarin exposure the dimeric tungstate undergoes monomerization, making coordinatively unsaturated Zr(IV) centers available, which activate nucleophilic hydrolysis. Further, DMCP is shown to be a good model system of reduced toxicity for studies of CWA deactivation at the gas–solid interface.
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Correlated Multimodal Approach Reveals Key Details of Nerve-Agent Decomposition by Single-Site Zr-Based Polyoxometalates
2019Co-Authors: Yiyao Tian, Anna M. Plonka, Amani M. Ebrahim, Robert M. Palomino, Sanjaya D. Senanayake, Alex Balboa, Wesley O. Gordon, Diego Troya, Djamaladdin G. Musaev, John R. MorrisAbstract:Development of technologies for protection against chemical warfare agents (CWAs) is critically important. Recently, polyoxometalates have attracted attention as potential catalysts for nerve-agent decomposition. Improvement of their effectiveness in real operating conditions requires an atomic-level understanding of CWA decomposition at the gas–solid interface. We investigated decomposition of the nerve agent Sarin and its simulant, dimethyl chlorophosphate (DMCP), by zirconium polytungstate. Using a Multimodal Approach, we showed that upon DMCP and Sarin exposure the dimeric tungstate undergoes monomerization, making coordinatively unsaturated Zr(IV) centers available, which activate nucleophilic hydrolysis. Further, DMCP is shown to be a good model system of reduced toxicity for studies of CWA deactivation at the gas–solid interface
Weilong Zheng - One of the best experts on this subject based on the ideXlab platform.
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a Multimodal Approach to estimating vigilance using eeg and forehead eog
Journal of Neural Engineering, 2017Co-Authors: Weilong Zheng, Baoliang LuAbstract:OBJECTIVE:Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estimating the vigilance of users using EEG and EOG signals. Approach:The PERCLOS index as vigilance annotation is obtained from eye tracking glasses. To improve the feasibility and wearability of vigilance estimation devices for real-world applications, we adopt a novel electrode placement for forehead EOG and extract various eye movement features, which contain the principal information of traditional EOG. We explore the effects of EEG from different brain areas and combine EEG and forehead EOG to leverage their complementary characteristics for vigilance estimation. Considering that the vigilance of users is a dynamic changing process because the intrinsic mental states of users involve temporal evolution, we introduce continuous conditional neural field and continuous conditional random field models to capture dynamic temporal dependency. MAIN RESULTS:We propose a Multimodal Approach to estimating vigilance by combining EEG and forehead EOG and incorporating the temporal dependency of vigilance into model training. The experimental results demonstrate that modality fusion can improve the performance compared with a single modality, EOG and EEG contain complementary information for vigilance estimation, and the temporal dependency-based models can enhance the performance of vigilance estimation. From the experimental results, we observe that theta and alpha frequency activities are increased, while gamma frequency activities are decreased in drowsy states in contrast to awake states. SIGNIFICANCE:The forehead setup allows for the simultaneous collection of EEG and EOG and achieves comparative performance using only four shared electrodes in comparison with the temporal and posterior sites.
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a Multimodal Approach to estimating vigilance using eeg and forehead eog
arXiv: Human-Computer Interaction, 2016Co-Authors: Weilong Zheng, Baoliang LuAbstract:Objective. Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estimating the vigilance of users using EEG and EOG signals. Approach. To improve the feasibility and wearability of vigilance estimation devices for real-world applications, we adopt a novel electrode placement for forehead EOG and extract various eye movement features, which contain the principal information of traditional EOG. We explore the effects of EEG from different brain areas and combine EEG and forehead EOG to leverage their complementary characteristics for vigilance estimation. Considering that the vigilance of users is a dynamic changing process because the intrinsic mental states of users involve temporal evolution, we introduce continuous conditional neural field and continuous conditional random field models to capture dynamic temporal dependency. Main results. We propose a Multimodal Approach to estimating vigilance by combining EEG and forehead EOG and incorporating the temporal dependency of vigilance into model training. The experimental results demonstrate that modality fusion can improve the performance compared with a single modality, EOG and EEG contain complementary information for vigilance estimation, and the temporal dependency-based models can enhance the performance of vigilance estimation. From the experimental results, we observe that theta and alpha frequency activities are increased, while gamma frequency activities are decreased in drowsy states in contrast to awake states. Significance. The forehead setup allows for the simultaneous collection of EEG and EOG and achieves comparative performance using only four shared electrodes in comparison with the temporal and posterior sites.
Jean Carrive - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - A Multimodal Approach to initialisation for top-down speaker diarization of television shows
2010Co-Authors: Simon Bozonnet, Félicien Vallet, Nicholas Evans, Slim Essid, Gael Richard, Jean CarriveAbstract:This paper presents a new Multimodal Approach to speaker diarization of TV show data. We hypothesize that the intraspeaker variation in visual information might be less than that in the corresponding acoustic information and therefore might be better suited to the task of speaker model initialisation. This is an acknowledged weakness of the computationally efficient top-down Approach to speaker diarization that is used here. Experimental results show that a recently proposed Approach to purification and the new Multimodal Approach to initialisation together deliver 22% and 17% relative improvements in diarization performance over the baseline system on independent development and evaluation datasets respectively.
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A Multimodal Approach TO INITIALISATION FOR TOP-DOWN SPEAKER DIARIZATION OF TELEVISION SHOWS
2010Co-Authors: Simon Bozonnet, Félicien Vallet, Nicholas Evans, Slim Essid, Gael Richard, Jean CarriveAbstract:This paper presents a new Multimodal Approach to speaker diarization of TV show data. We hypothesize that the intra-speaker variation in visual information might be less than that in the corresponding acoustic information and therefore might be better suited to the task of speaker model initialisa-tion. This is an acknowledged weakness of the computation-ally efficient top-down Approach to speaker diarization that is used here. Experimental results show that a recently proposed Approach to purification and the new Multimodal Approach to initialisation together deliver 22% and 17% relative improvements in diarization performance over the baseline system on independent development and evaluation datasets respectively.
