A system that can detect the intention to move and decode the planned movement could help all those subjects that can plan motion but are unable to implement it. In this paper, motor planning activity is investigated by using electroencephalographic (EEG) signals with the aim to decode motor preparation phases. A publicly available database of 61-channels EEG signals recorded from 15 healthy subjects during the execution of different movements (elbow flexion/extension, forearm pronation/supination, hand open/close) of the right upper limb was employed to generate a dataset of EEG epochs preceding resting and movement's onset. A novel system is introduced for the classification of premovement vs resting and of premovement vs premovement epochs. For every epoch, the proposed system generates a time-frequency (TF) map of every source signal in the motor cortex, through beamforming and Continuous Wavelet Transform (CWT), then all the maps are embedded in a volume and used as input to a deep CNN. The proposed system succeeded in discriminating premovement from resting with an average accuracy of 90.3% (min 74.6%, max 100%), outperforming comparable methods in the literature, and in discriminating premovement vs premovement with an average accuracy of 62.47%. The achieved results encourage to investigate motor planning at source level in the time-frequency domain through deep learning approaches.

中文翻译：

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一种深度CNN方法，可从源水平的EEG信号的时频图上解码上肢的运动准备。

一个可以检测到运动意图并解码所计划运动的系统可以帮助所有可以计划运动但无法实现运动的对象。在本文中，通过使用脑电图（EEG）信号来研究运动计划活动，目的是解码运动准备阶段。使用一个可公开获取的数据库，该数据库从15位健康受试者在执行右上肢的不同运动（肘部弯曲/伸展，前臂内旋/旋前，手张开/闭合）期间记录的61通道EEG信号用于生成EEG数据集休息和运动开始之前的时期。引入了一种新颖的系统，用于对运动前与休息以及运动前与运动时期进行分类。在每个时代，拟议的系统通过波束成形和连续小波变换（CWT）生成运动皮质中每个源信号的时频（TF）图，然后将所有图嵌入到一个体积中，并用作深层CNN的输入。所提出的系统以90.3％的平均准确度（最小74.6％，最大100％）成功地将运动与休息区分开，优于文献中的同类方法，并以62.47％的平均精度将运动与运动区分开。所取得的结果鼓励通过深度学习方法在时频域中从源水平上研究运动计划。所提出的系统以90.3％的平均准确度（最小74.6％，最大100％）成功地将运动与休息区分开，优于文献中的同类方法，并且以62.47％的平均准确度对运动与运动进行区分。所取得的结果鼓励通过深度学习方法在时频域中从源水平上研究运动计划。所提出的系统以90.3％的平均准确度（最小74.6％，最大100％）成功地将运动与休息区分开，优于文献中的同类方法，并且以62.47％的平均准确度对运动与运动进行区分。所取得的结果鼓励通过深度学习方法在时频域中从源水平上研究运动计划。