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首页> 外文期刊>The European Journal of Neuroscience >Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity
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Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity

机译：从人脑活动的单次试验性磁共振成像记录中解码单个手指的运动

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Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement. We explored the decoding of individual finger movements using fMRI, and the multivariate pattern classification analysis was based on single trial data. We found that the decoding accuracies in the brain ROI of left S1 and M1 were significantly above the chance level. This study might allow a better understanding the basis neural mechanism controlling the movements of individual fingers and may provide new insights into the potential of an fMRI-based BMI for finger movements.
机译：多元模式分类分析（MVPA）已应用于功能磁共振成像（fMRI）数据，以从空间分布的激活模式中解码大脑状态。从无创记录的人脑激活中解码上肢运动对于实现直接利用个人思想控制外部设备或计算机的脑机接口至关重要。这项研究的目的是从fMRI单次试验数据中解码出各个手指的动作。 13位健康的人类受试者参加了视觉提示的手指延迟运动任务，并且在每个试验中仅执行了一次轻按按钮。使用MVPA，针对感兴趣的不同电机相关区域分别计算了解码精度（DA）。为了构建特征向量，将来自图像序列中两个连续体积的特征向量进行了级联。利用这些时空特征向量，对侧初级体感皮层的平均DA为63.1％（最佳受试者为84.7％），对侧初级运动皮层的平均DA为46.0％（最佳受试者为71.0％）。这两个值均显着高于机会水平（20％）。此外，我们实施了探照灯MVPA，以在整个大脑中无偏见地搜索信息区域。此外，通过将探照灯MVPA应用于试验的每个卷，我们在视觉上展示了用于解码的信息（时空上）。结果表明，非侵入性功能磁共振成像技术可以为解码单个手指运动提供信息功能，并为手指运动开发基于功能磁共振成像的脑机接口的潜力。我们探索了使用功能磁共振成像对单个手指运动的解码，并且基于单个试验数据进行了多模式分类分析。我们发现左S1和M1的大脑ROI的解码精度明显高于机会水平。这项研究可能会更好地理解控制单个手指运动的基本神经机制，并可能为基于fMRI的BMI对手指运动的潜力提供新的见解。

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