Zabolotniy Aleksey, Chan Russell Weili, Moiseeva Victoria, Fedele Tommaso
Institute of Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia.
Department of Learning, Data Analytics and Technology, Section Cognition, Data and Education, Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, Netherlands.
Front Neurosci. 2025 Sep 9;19:1623380. doi: 10.3389/fnins.2025.1623380. eCollection 2025.
Non-invasive Brain-Computer Interfaces provide accurate classification of hand movement lateralization. However, distinguishing activation patterns of individual fingers within the same hand remains challenging due to their overlapping representations in the motor cortex. Here, we validated a compact convolutional neural network for fast and reliable decoding of finger movements from non-invasive magnetoencephalographic (MEG) recordings.
We recorded healthy participants in MEG performing a serial reaction time task (SRTT), with buttons pressed by left and right index and middle fingers. We devised classifiers to identify left vs. right hand movements and among four finger movements using a recently proposed decoding approach, Linear Finite Impulse Response Convolutional Neural Network (LF-CNN). We also compared LF-CNN to existing deep learning architectures such as EEGNet, FBCSP-ShallowNet, and VGG19.
Sequence learning was reflected by a decrease in reaction times during SRTT performance. Movement laterality was decoded with an accuracy superior to 95% by all approaches, while for individual finger movement, decoding was in the 80-85% range. LF-CNN stood out for (1) its low computational time and (2) its interpretability in both spatial and spectral domains, allowing to examine neurophysiological patterns reflecting task-related motor cortex activity.
We demonstrated the feasibility of finger movement decoding with a tailored Convolutional Neural Network. The performance of our approach was comparable to complex deep learning architectures, while providing faster and interpretable outcome. This algorithmic strategy holds high potential for the investigation of the mechanisms underlying non-invasive neurophysiological recordings in cognitive neuroscience.
非侵入性脑机接口可对手部运动的方向进行准确分类。然而，由于同一手部内各个手指在运动皮层中的表征存在重叠，区分单个手指的激活模式仍然具有挑战性。在此，我们验证了一种紧凑的卷积神经网络，用于从非侵入性脑磁图（MEG）记录中快速可靠地解码手指运动。
我们记录了健康参与者在进行序列反应时任务（SRTT）时的脑磁图数据，他们用左右食指和中指按压按钮。我们设计了分类器，使用最近提出的解码方法——线性有限脉冲响应卷积神经网络（LF-CNN），来识别左手与右手运动以及四种手指运动。我们还将LF-CNN与现有的深度学习架构（如EEGNet、FBCSP-ShallowNet和VGG19）进行了比较。
在执行SRTT任务期间，反应时间的减少反映了序列学习情况。所有方法对手部运动方向的解码准确率均优于95%，而对于单个手指运动，解码准确率在80%-85%范围内。LF-CNN的突出之处在于：（1）计算时间短；（2）在空间和频谱域均具有可解释性，能够检查反映与任务相关的运动皮层活动的神经生理模式。
我们证明了使用定制的卷积神经网络对手指运动进行解码的可行性。我们方法的性能与复杂的深度学习架构相当，同时能提供更快且可解释的结果。这种算法策略在认知神经科学中对非侵入性神经生理记录背后机制的研究具有很大潜力。
