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Perception and Cognition of Cues Used in Synchronous Brain–Computer Interfaces Modify Electroencephalographic Patterns of Control Tasks

机译:感知和认知的同步脑计算机接口中使用的提示修改控制任务的脑电图模式。

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摘要

A motor imagery (MI)-based brain–computer interface (BCI) is a system that enables humans to interact with their environment by translating their brain signals into control commands for a target device. In particular, synchronous BCI systems make use of cues to trigger the motor activity of interest. So far, it has been shown that electroencephalographic (EEG) patterns before and after cue onset can reveal the user cognitive state and enhance the discrimination of MI-related control tasks. However, there has been no detailed investigation of the nature of those EEG patterns. We, therefore, propose to study the cue effects on MI-related control tasks by selecting EEG patterns that best discriminate such control tasks, and analyzing where those patterns are coming from. The study was carried out using two methods: standard and all-embracing. The standard method was based on sources (recording sites, frequency bands, and time windows), where the modulation of EEG signals due to motor activity is typically detected. The all-embracing method included a wider variety of sources, where not only motor activity is reflected. The findings of this study showed that the classification accuracy (CA) of MI-related control tasks did not depend on the type of cue in use. However, EEG patterns that best differentiated those control tasks emerged from sources well defined by the perception and cognition of the cue in use. An implication of this study is the possibility of obtaining different control commands that could be detected with the same accuracy. Since different cues trigger control tasks that yield similar CAs, and those control tasks produce EEG patterns differentiated by the cue nature, this leads to accelerate the brain–computer communication by having a wider variety of detectable control commands. This is an important issue for Neuroergonomics research because neural activity could not only be used to monitor the human mental state as is typically done, but this activity might be also employed to control the system of interest.
机译:基于运动图像(MI)的脑机接口(BCI)是一种系统,该系统使人类能够通过将其大脑信号转换为目标设备的控制命令来与周围环境进行交互。尤其是,同步BCI系统利用提示来触发感兴趣的电机活动。到目前为止,已经表明提示发作前后的脑电图(EEG)模式可以揭示用户的认知状态并增强对MI相关控制任务的区分。但是,尚未对这些脑电图模式的性质进行详细调查。因此,我们建议通过选择最能区分此类控制任务的EEG模式并分析这些模式的来源来研究对MI相关控制任务的提示效果。该研究使用两种方法进行:标准方法和全能方法。标准方法基于来源(记录站点,频带和时间窗口),在这些来源中,通常会检测到由于运动引起的EEG信号的调制。无所不包的方法包括各种各样的来源,不仅反映了运动活动。这项研究的结果表明,与MI相关的控制任务的分类准确性(CA)不取决于所使用提示的类型。但是,最能区分这些控制任务的EEG模式来自于对使用中的提示的感知和认知所明确定义的来源。这项研究的含义是获得不同的控制命令的可能性,这些命令可以相同的精度被检测到。由于不同的提示会触发产生相似CA的控制任务,并且这些控制任务会产生因提示性质而异的EEG模式,因此通过拥有更多种类的可检测控制命令,可以加速脑机通信。这是神经人体工程学研究的重要问题,因为神经活动不仅可以像通常那样用于监视人的精神状态,而且还可以用于控制目标系统。

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