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首页> 美国卫生研究院文献>Frontiers in Human Neuroscience >Co-adaptive Training Improves Efficacy of a Multi-Day EEG-Based Motor Imagery BCI Training
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Co-adaptive Training Improves Efficacy of a Multi-Day EEG-Based Motor Imagery BCI Training

机译：自适应培训提高了基于脑电图的多日运动图像BCI培训的效率

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Motor imagery (MI) based brain computer interfaces (BCI) detect changes in brain activity associated with imaginary limb movements, and translate them into device commands. MI based BCIs require training, during which the user gradually learns how to control his or her brain activity with the help of feedback. Additionally, machine learning techniques are frequently used to boost BCI performance and to adapt the decoding algorithm to the user's brain. Thus, both the brain and the machine need to adapt in order to improve performance. To study the utility of co-adaptive training in the BCI paradigm and the time scales involved, we investigated the performance of two groups of subjects, in a 4-day MI experiment using EEG recordings. One group (control, n = 9 subjects) performed the BCI task using a fixed classifier based on MI data from day 1. In the second group (experimental, n = 9 subjects), the classifier was regularly adapted based on brain activity patterns during the experiment days. We found that the experimental group showed a significantly larger change in performance following training compared to the control group. Specifically, although the experimental group exhibited a decrease in performance between days, it showed an increase in performance within each day, which compensated for the decrease. The control group showed decreases both within and between days. A correlation analysis in subjects who had a notable improvement in performance following training showed that performance was mainly associated with modulation of power in the α frequency band. To conclude, continuous updating of the classification algorithm improves the performance of subjects in longitudinal BCI training.
机译：基于运动图像（MI）的大脑计算机接口（BCI）可检测与虚构肢体运动相关的大脑活动的变化，并将其转换为设备命令。基于MI的BCI需要进行培训，在此期间，用户逐步学习如何在反馈的帮助下控制其大脑活动。此外，机器学习技术通常用于提高BCI性能并使解码算法适应用户的大脑。因此，大脑和机器都需要适应才能改善性能。为了研究BCI范例中的自适应训练的效用和涉及的时间尺度，我们在4天的MI实验中使用EEG记录调查了两组受试者的表现。一组（对照组，n = 9名受试者）根据第1天的MI数据使用固定分类器执行BCI任务。在第二组（实验组，n = 9名受试者）中，根据脑部活动模式定期对分类器进行调整实验的日子。我们发现，与对照组相比，实验组的训练后表现出明显更大的变化。具体而言，尽管实验组在两天之间表现出降低的表现，但是在每一天中表现出表现的提高，这弥补了该下降。对照组在几天之内和之间都显示出减少。对训练后表现显着改善的受试者进行的相关分析表明，表现主要与α频段功率调制有关。总而言之，分类算法的持续更新提高了纵向BCI训练中受试者的表现。

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