Department of Electronics Engineering, Chosun University, 309 Pilmundae-ro, Dong-gu, Gwangju, 61452, Republic of Korea.
Interdisciplinary Program in IT-Bio Convergence System, Chosun University, Gwangju, 61452, Republic of Korea.
Sci Data. 2023 Aug 22;10(1):552. doi: 10.1038/s41597-023-02454-y.
Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (3 mm) and temporal resolution (1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements.
研究大脑的运动控制机制在学术界至关重要，并且具有实际意义，因为可以基于大脑机制开发脑机接口 (BCI) 等技术。脑磁图 (MEG) 信号在非侵入性方法中具有最高的空间分辨率（约 3 毫米）和时间分辨率（约 1 毫秒）。因此，MEG 是研究大脑机制的绝佳方式。然而，由于昂贵的 MEG 设备、需要磁屏蔽室以及氦气供应的高维护成本，公开可用的 MEG 数据仍然稀缺。在这项研究中，我们共享了在三维伸手运动过程中采集的 306 通道 MEG 和三轴加速度计信号。此外，我们还提供了时频分析、F 值时频分析和地形图分析的分析结果和 MATLAB 代码。这些共享的 MEG 数据集为研究大脑活动或评估预测算法的准确性提供了有价值的资源。据我们所知，这是唯一公开可用的在伸手运动过程中测量的 MEG 数据。
