Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
Institute of Neural Engineering, Graz University of Technology, Graz, Austria; BioTechMed Graz, Austria.
J Neurosci Methods. 2024 Oct;410:110241. doi: 10.1016/j.jneumeth.2024.110241. Epub 2024 Aug 5.
In electroencephalographic (EEG) or electrocorticographic (ECoG) experiments, visual cues are commonly used for timing synchronization but may inadvertently induce neural activity and cognitive processing, posing challenges when decoding self-initiated tasks.
To address this concern, we introduced four new visual cues (Fade, Rotation, Reference, and Star) and investigated their impact on brain signals. Our objective was to identify a cue that minimizes its influence on brain activity, facilitating cue-effect free classifier training for asynchronous applications, particularly aiding individuals with severe paralysis.
22 able-bodied, right-handed participants aged 18-30 performed hand movements upon presentation of the visual cues. Analysis of time-variability between movement onset and cue-aligned data, grand average MRCP, and classification outcomes revealed significant differences among cues. Rotation and Reference cue exhibited favorable results in minimizing temporal variability, maintaining MRCP patterns, and achieving comparable accuracy to self-paced signals in classification.
Our study contrasts with traditional cue-based paradigms by introducing novel visual cues designed to mitigate unintended neural activity. We demonstrate the effectiveness of Rotation and Reference cue in eliciting consistent and accurate MRCPs during motor tasks, surpassing previous methods in achieving precise timing and high discriminability for classifier training.
Precision in cue timing is crucial for training classifiers, where both Rotation and Reference cue demonstrate minimal variability and high discriminability, highlighting their potential for accurate classifications in online scenarios. These findings offer promising avenues for refining brain-computer interface systems, particularly for individuals with motor impairments, by enabling more reliable and intuitive control mechanisms.
在脑电图（EEG）或皮质电图（ECoG）实验中，视觉线索通常用于时间同步，但可能会无意中引起神经活动和认知处理，这在解码自主任务时带来了挑战。
为了解决这个问题，我们引入了四个新的视觉线索（Fade、Rotation、Reference 和 Star），并研究了它们对脑信号的影响。我们的目标是确定一个最小化其对脑活动影响的线索，为异步应用程序（特别是帮助严重瘫痪的个体）提供无 cue 效应的分类器训练。
22 名 18-30 岁的右利手健康参与者在手出现视觉线索时进行了手部运动。对运动起始和线索对齐数据之间的时变分析、平均 MRCP 和分类结果进行了分析，结果表明线索之间存在显著差异。Rotation 和 Reference 线索在最小化时间可变性、保持 MRCP 模式以及在分类中达到与自我调节信号相当的准确性方面表现出良好的结果。
我们的研究通过引入旨在减轻无意神经活动的新型视觉线索，与传统基于 cue 的范式形成对比。我们证明了 Rotation 和 Reference 线索在诱发运动任务中一致且准确的 MRCP 的有效性，在实现精确计时和高分类器训练可区分性方面超过了以前的方法。
在训练分类器时，cue 时间的精度至关重要，Rotation 和 Reference 线索都表现出最小的可变性和高的可区分性，这突出了它们在在线场景中进行准确分类的潜力。这些发现为改进脑机接口系统提供了有前途的途径，特别是对于运动障碍个体，通过实现更可靠和直观的控制机制。
