Kuc Alexander, Korchagin Sergey, Maksimenko Vladimir A, Shusharina Natalia, Hramov Alexander E
Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.
Department of Data Analysis and Machine Learning, Financial University Under the Government of the Russian Federation, Moscow, Russia.
Front Syst Neurosci. 2021 Nov 16;15:716897. doi: 10.3389/fnsys.2021.716897. eCollection 2021.
Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group of subjects. As a result, the pre-trained classifier achieved 74% accuracy on the data of newly recruited subjects. Analysis of the literature suggested that a pre-trained classifier could help naive users to start using BCI bypassing training and further increased accuracy during the feedback session. Thus, our results contribute to using BCI during paralysis or limb amputation when there is no explicit user-generated kinematic output to properly train a decoder. In machine learning, our approach may facilitate the development of transfer learning (TL) methods for addressing the cross-subject problem. It allows extracting the interpretable feature subspace from the source data (the representative group of subjects) related to the target data (a naive user), preventing the negative transfer in the cross-subject tasks.
将脑机接口（BCI）融入日常生活需要减少解码算法对校准的依赖，或者在给用户带来最小负担的情况下实现校准。一个潜在的解决方案可能是一个预训练解码器，它在未经训练的操作者身上能展现出合理的准确性。为了解决这个问题，我们考虑了模糊刺激分类任务，并训练了一个人工神经网络来对大脑对低模糊度和高模糊度刺激的反应进行分类。我们利用与受试者之间共享的基本神经生理过程相对应的时频特征构建了一个预训练分类器。为了提取这些特征，我们对代表性受试者组中不同类别之间的脑电图（EEG）频谱功率进行了统计对比。结果，预训练分类器在新招募受试者的数据上达到了74%的准确率。文献分析表明，预训练分类器可以帮助未经训练的用户在绕过训练的情况下开始使用BCI，并在反馈过程中进一步提高准确率。因此，我们的结果有助于在瘫痪或肢体截肢期间使用BCI，此时没有明确的用户生成的运动输出以正确训练解码器。在机器学习中，我们的方法可能有助于开发用于解决跨受试者问题的迁移学习（TL）方法。它允许从与目标数据（未经训练的用户）相关的源数据（代表性受试者组）中提取可解释的特征子空间，防止在跨受试者任务中出现负迁移。
