Computational Brain Research and Intervention (C-BRAIN) Laboratory, Department of Psychiatry and Behavioral Sciences, C-Brain Lab, School of Medicine, Stanford University, 1520 Page Mill Rd, Palo Alto, CA 94304, United States.
Department of Cognitive and Information Sciences, University of California, Merced, 5200 North Lake Road, Merced, CA 95343, United States.
Cereb Cortex. 2024 Oct 3;34(10). doi: 10.1093/cercor/bhae427.
Research on action-based timing has shed light on the temporal dynamics of sensorimotor coordination. This study investigates the neural mechanisms underlying action-based timing, particularly during finger-tapping tasks involving synchronized and syncopated patterns. Twelve healthy participants completed a continuation task, alternating between tapping in time with an auditory metronome (pacing) and continuing without it (continuation). Electroencephalography data were collected to explore how neural activity changes across these coordination modes and phases. We applied deep learning methods to classify single-trial electroencephalography data and predict behavioral timing conditions. Results showed significant classification accuracy for distinguishing between pacing and continuation phases, particularly during the presence of auditory cues, emphasizing the role of auditory input in motor timing. However, when auditory components were removed from the electroencephalography data, the differentiation between phases became inconclusive. Mean accuracy asynchrony, a measure of timing error, emerged as a superior predictor of performance variability compared to inter-response interval. These findings highlight the importance of auditory cues in modulating motor timing behaviors and present the challenges of isolating motor activation in the absence of auditory stimuli. Our study offers new insights into the neural dynamics of motor timing and demonstrates the utility of deep learning in analyzing single-trial electroencephalography data.
基于动作的时间研究揭示了运动感觉协调的时间动态。本研究探讨了基于动作的时间的神经机制，特别是在涉及同步和切分模式的手指敲击任务中。12 名健康参与者完成了一项延续任务，在与听觉节拍器（定速）同步敲击和不跟随节拍器敲击之间交替进行（延续）。采集了脑电图数据，以探索在这些协调模式和阶段中神经活动如何变化。我们应用深度学习方法对单试脑电图数据进行分类，并预测行为时间条件。结果表明，在区分定速和延续阶段方面具有显著的分类准确性，尤其是在存在听觉线索的情况下，这强调了听觉输入在运动计时中的作用。然而，当从脑电图数据中去除听觉成分时，各阶段之间的区分变得不确定。平均准确性时滞，作为一种衡量时间误差的指标，与反应间间隔相比，更能预测表现变异性。这些发现强调了听觉线索在调节运动计时行为中的重要性，并提出了在没有听觉刺激的情况下分离运动激活的挑战。我们的研究为运动计时的神经动力学提供了新的见解，并展示了深度学习在分析单试脑电图数据方面的实用性。
