Alonso-Valerdi Luz María, Sepulveda Francisco, Ramírez-Mendoza Ricardo A
Brain-Computer Interfaces (BCI) Group, School of Computing Science and Electronic Engineering, University of Essex , Colchester , UK ; Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey - Campus Ciudad de México , Mexico City , Mexico.
Brain-Computer Interfaces (BCI) Group, School of Computing Science and Electronic Engineering, University of Essex , Colchester , UK.
Front Hum Neurosci. 2015 Nov 23;9:636. doi: 10.3389/fnhum.2015.00636. eCollection 2015.
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.
基于运动想象（MI）的脑机接口（BCI）是一种系统，它通过将大脑信号转化为目标设备的控制指令，使人类能够与周围环境进行交互。特别是，同步BCI系统利用线索来触发感兴趣的运动活动。到目前为止，已经表明线索开始前后的脑电图（EEG）模式可以揭示用户的认知状态，并增强对与MI相关的控制任务的辨别能力。然而，尚未对这些EEG模式的本质进行详细研究。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，来研究线索对与MI相关的控制任务的影响。该研究使用了两种方法：标准方法和全面方法。标准方法基于源（记录位点、频段和时间窗），在这些源中通常检测到由于运动活动引起的EEG信号调制。全面方法包括更广泛的源，其中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率（CA）并不取决于所使用的线索类型。然而，最能区分这些控制任务的EEG模式来自于由所使用线索的感知和认知明确界定的源。这项研究的一个启示是，有可能获得以相同准确率检测到的不同控制指令。由于不同的线索触发产生相似CA的控制任务，并且这些控制任务产生由线索性质区分的EEG模式，这导致通过拥有更广泛的可检测控制指令来加速脑机通信。这对于神经工效学研究是一个重要问题，因为神经活动不仅可以像通常那样用于监测人类的心理状态，而且这种活动还可能被用于控制系统。
