Objective. Brain-computer interfaces (BCIs) using electrocorticographic (ECoG) signals have been developed to restore the communication function of severely paralyzed patients. However, the limited amount of information derived from ECoG signals hinders their clinical applications. We aimed to develop a method to decode ECoG signals using spatiotemporal patterns characterizing movement types to increase the amount of information gained from these signals. Approach . Previous studies have demonstrated that motor information could be decoded using powers of specific frequency bands of the ECoG signals estimated by fast Fourier transform (FFT) or wavelet analysis. However, because FFT is evaluated for each channel, the temporal and spatial patterns among channels are difficult to evaluate. Here, we used dynamic mode decomposition (DMD) to evaluate the spatiotemporal pattern of ECoG signals and evaluated the accuracy of motor decoding with the DMD modes. We used ECoG s...

中文翻译：

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使用动态模式分解对皮层电信号进行神经解码。

目的。已经开发了使用脑电图（ECoG）信号的脑机接口（BCI），以恢复严重瘫痪患者的通讯功能。但是，从ECoG信号获得的信息量有限，阻碍了其临床应用。我们旨在开发一种使用时空模式来表征运动类型的ECoG信号解码方法，以增加从这些信号中获取的信息量。方法。先前的研究表明，可以使用通过快速傅里叶变换（FFT）或小波分析估计的ECoG信号特定频带的功率来解码电动机信息。但是，由于对每个通道都进行了FFT评估，因此很难评估通道之间的时间和空间模式。这里，我们使用动态模式分解（DMD）来评估ECoG信号的时空模式，并使用DMD模式来评估电机解码的准确性。我们使用了ECoG软件