Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals
Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals
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DOI：
10.1523/jneurosci.6107-09.2010
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发表时间：
2010-03-03
影响因子：
5.3
通讯作者：
Contreras-Vidal, Jose L.
中科院分区：
文献类型：
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作者：
Bradberry, Trent J.;Gentili, Rodolphe J.;Contreras-Vidal, Jose L.
一般认为，通过非侵入性头皮脑电图（EEG）采集的神经数据的信噪比、带宽和信息内容不足以提取关于上肢的自然多关节运动的详细信息。在这里，我们挑战这一假设，连续解码三维（3D）的手速度从头皮与55通道EEG在3D中心达到任务期间获得的神经数据。为了保持生态有效性，五个研究对象自发达成并自选目标。眼球运动受到控制，因此不会混淆结果的解释。只有34个传感器，测量和重建的速度曲线之间的相关性比较合理，以及解码的手运动学从颅内获得的神经活动的研究报告。随后，我们研究了EEG传感器对解码的贡献，发现对侧的感觉运动皮层的头皮区域大量参与。使用标准化的低分辨率脑电磁断层扫描（sLORETA），我们确定了分布的电流密度源相关的手速度在对侧中央前回，中央后回，顶下小叶。此外，我们发现运动变异性与解码准确性呈负相关，这是脑机接口系统开发过程中需要考虑的一个发现。总的来说，在自然的，中心向外的延伸过程中，从EEG连续解码3D手部速度的能力为运动障碍个体的非侵入性神经运动假体的进一步发展带来了希望。
It is generally thought that the signal-to-noise ratio, the bandwidth, and the information content of neural data acquired via noninvasive scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multijoint movements of the upper limb. Here, we challenge this assumption by continuously decoding three-dimensional (3D) hand velocity from neural data acquired from the scalp with 55-channel EEG during a 3D center-out reaching task. To preserve ecological validity, five subjects self-initiated reaches and self-selected targets. Eye movements were controlled so they would not confound the interpretation of the results. With only 34 sensors, the correlation between measured and reconstructed velocity profiles compared reasonably well to that reported by studies that decoded hand kinematics from neural activity acquired intracranially. We subsequently examined the individual contributions of EEG sensors to decoding to find substantial involvement of scalp areas over the sensorimotor cortex contralateral to the reaching hand. Using standardized low-resolution brain electromagnetic tomography (sLORETA), we identified distributed current density sources related to hand velocity in the contralateral precentral gyrus, postcentral gyrus, and inferior parietal lobule. Furthermore, we discovered that movement variability negatively correlated with decoding accuracy, a finding to consider during the development of brain-computer interface systems. Overall, the ability to continuously decode 3D hand velocity from EEG during natural, center-out reaching holds promise for the furtherance of noninvasive neuromotor prostheses for movement-impaired individuals.