Sonntag Justin, Yu Lin, Wang Xilu, Schack Thomas
Neurocognition and Action - Biomechanics Research Group, Faculty of Psychology and Sports Science, Bielefeld University, Bielefeld, Germany.
Computer Science Research Centre, University of Surrey, Guildford, United Kingdom.
Front Hum Neurosci. 2025 Jul 4;19:1617748. doi: 10.3389/fnhum.2025.1617748. eCollection 2025.
Motor imagery-based brain-computer interfaces (BCIs) are a technique for decoding and classifying the intention of motor execution, solely based on imagined (rather than executed) movements. Although deep learning techniques have increased the potential of BCIs, the complexity of decoding unilateral upper limb motor imagery remains challenging. To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.
In this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-based machine learning classifiers (SVM, LDA) were used to classify imagined right elbow flexion and extension from participants using electroencephalography data. From two recorded resting states (eyes-open, eyes-closed), absolute and relative alpha and beta power of the frontal, fronto-central and central electrodes were used to predict the accuracy of the different classifiers.
The prediction of classifier accuracies by neurophysiological features revealed negative correlations between the relative alpha band and classifier accuracies and positive correlations between the absolute and relative beta band and classifiers accuracies. Most ipsilateral EEG channels yielded significant correlations with classifier accuracies, especially for the machine learning classifier.
This pattern contrasts with previous findings from bilateral MI paradigms, where contralateral alpha and beta activity were more influential. These inverted correlations suggest task-specific neurophysiological mechanisms in unilateral MI, emphasizing the role of ipsilateral inhibition and attentional processes.
基于运动想象的脑机接口（BCI）是一种仅基于想象（而非实际执行）的运动来解码和分类运动执行意图的技术。尽管深度学习技术提高了BCI的潜力，但解码单侧上肢运动想象的复杂性仍然具有挑战性。为了了解与运动想象神经机制直接相关的神经生理特征是否可能影响分类准确性，大多数研究主要利用传统机器学习框架，而基于深度学习的技术尚未得到充分探索。
在这项工作中，使用了文献中的三种不同深度学习模型（EEGNet、FBCNet、NFEEG）和两种基于共同空间模式的机器学习分类器（支持向量机、线性判别分析），根据参与者的脑电图数据对想象的右肘屈伸进行分类。从记录的两种静息状态（睁眼、闭眼）中，利用额部、额中央和中央电极的绝对和相对α波和β波功率来预测不同分类器的准确性。
神经生理特征对分类器准确性的预测显示，相对α波段与分类器准确性之间呈负相关，绝对和相对β波段与分类器准确性之间呈正相关。大多数同侧脑电图通道与分类器准确性有显著相关性，尤其是对于机器学习分类器。
这种模式与之前双侧运动想象范式的研究结果形成对比，在双侧运动想象范式中，对侧α波和β波活动的影响更大。这些反向相关性表明单侧运动想象中存在特定任务的神经生理机制，强调了同侧抑制和注意力过程的作用。
