Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training
Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training
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DOI：
10.3389/fnins.2020.00623
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发表时间：
2020-06-24
影响因子：
4.3
通讯作者：
Nakai, Toshiharu
中科院分区：
文献类型：
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作者：
Bagarinao, Epifanio;Yoshida, Akihiro;Nakai, Toshiharu
在这项研究中，我们研究了神经反馈训练过程中大脑激活的动态变化对与目标任务相关的不同大脑状态分类的影响。我们假设，由于学习效应，正在进行的激活模式可能会在神经反馈会话期间发生变化，并且在此过程中，可能会影响使用会话之前获得的数据训练的大脑状态分类器的性能。使用运动想象范式，然后我们研究了增量训练方法的应用，其中分类器不断更新，以考虑这些激活变化。我们的研究结果证实了我们的假设，即神经反馈训练可能与大脑激活的动态变化有关，其特征是最初更广泛的大脑激活，然后是更集中和局部的激活模式。通过在每次反馈运行后不断更新训练的分类器，实现了对与目标运动想象任务相关联的不同大脑状态进行准确分类的显著改进。这些发现表明，在神经反馈过程中考虑大脑激活变化的重要性，以便为参与者提供更可靠和准确的反馈信息，这对于有效的神经反馈应用至关重要。
In this study, we investigated the effect of the dynamic changes in brain activation during neurofeedback training in the classification of the different brain states associated with the target tasks. We hypothesized that ongoing activation patterns could change during neurofeedback session due to learning effects and, in the process, could affect the performance of brain state classifiers trained using data obtained prior to the session. Using a motor imagery paradigm, we then examined the application of an incremental training approach where classifiers were continuously updated in order to account for these activation changes. Our results confirmed our hypothesis that neurofeedback training could be associated with dynamic changes in brain activation characterized by an initially more widespread brain activation followed by a more focused and localized activation pattern. By continuously updating the trained classifiers after each feedback run, significant improvement in accurately classifying the different brain states associated with the target motor imagery tasks was achieved. These findings suggest the importance of taking into account brain activation changes during neurofeedback in order to provide more reliable and accurate feedback information to the participants, which is critical for an effective neurofeedback application.