Bernstein Center Freiburg, University of Freiburg, Hansastr. 9A, 79104 Freiburg, Germany.
J Neural Eng. 2012 Apr;9(2):026007. doi: 10.1088/1741-2560/9/2/026007. Epub 2012 Feb 13.
Brain-machine interface (BMI) devices make errors in decoding. Detecting these errors online from neuronal activity can improve BMI performance by modifying the decoding algorithm and by correcting the errors made. Here, we study the neuronal correlates of two different types of errors which can both be employed in BMI: (i) the execution error, due to inaccurate decoding of the subjects' movement intention; (ii) the outcome error, due to not achieving the goal of the movement. We demonstrate that, in electrocorticographic (ECoG) recordings from the surface of the human brain, strong error-related neural responses (ERNRs) for both types of errors can be observed. ERNRs were present in the low and high frequency components of the ECoG signals, with both signal components carrying partially independent information. Moreover, the observed ERNRs can be used to discriminate between error types, with high accuracy (≥83%) obtained already from single electrode signals. We found ERNRs in multiple cortical areas, including motor and somatosensory cortex. As the motor cortex is the primary target area for recording control signals for a BMI, an adaptive motor BMI utilizing these error signals may not require additional electrode implants in other brain areas.
脑机接口（BMI）设备在解码时会出现错误。通过修改解码算法和纠正错误，从神经元活动中在线检测这些错误可以提高 BMI 的性能。在这里，我们研究了两种不同类型的错误的神经元相关性，这两种错误都可以应用于 BMI：（i）执行错误，由于对受试者运动意图的解码不准确；（ii）结果错误，由于未达到运动的目标。我们证明，在人类大脑表面的脑电描记图（ECoG）记录中，可以观察到这两种类型的错误都存在强烈的与错误相关的神经反应（ERNR）。ERNR 存在于 ECoG 信号的低频和高频分量中，这两个信号分量携带部分独立的信息。此外，所观察到的 ERNR 可用于区分错误类型，即使仅从单个电极信号也能获得高精度（≥83%）。我们在多个皮层区域中发现了 ERNR，包括运动和体感皮层。由于运动皮层是记录 BMI 控制信号的主要目标区域，因此利用这些错误信号的自适应运动 BMI 可能不需要在其他脑区植入额外的电极。
