论文标题
基于时间序列预测范式的运动图像发作检测低潜伏期的自定进度BCI系统
A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm
论文作者
论文摘要
在自定进度的运动象征脑计算机界面（MI-BCI）中，在连续的脑电图（EEG）信号中呈现的MI命令的打击是未知的。为了检测这些发动机，大多数自定进度的方法在连续的脑电图信号上应用窗口函数，并将其分成长段以进行进一步分析。结果，系统具有很高的延迟。为了减少系统延迟，我们根据时间序列预测概念提出了一种算法，并使用先前接收的时间样本的数据来预测即将到来的时间样本。我们的预测指标是一个具有长短期内存（LSTM）单元的编码器 - 编码器（ED）网络。通过将传入信号与预测的信号进行比较，很快就检测到MI命令的打击。该方法在BCI竞争III的数据集IVC上进行了验证。仿真结果表明，拟议的算法将竞争者的平均F1得分提高了26.7％的延迟，而不是一秒钟。
In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsets of the MI commands presented in a continuous electroencephalogram (EEG) signal are unknown. To detect these onsets, most self-paced approaches apply a window function on the continuous EEG signal and split it into long segments for further analysis. As a result, the system has a high latency. To reduce the system latency, we propose an algorithm based on the time series prediction concept and use the data of the previously received time samples to predict the upcoming time samples. Our predictor is an encoder-decoder (ED) network built with long short-term memory (LSTM) units. The onsets of the MI commands are detected shortly by comparing the incoming signal with the predicted signal. The proposed method is validated on dataset IVc from BCI competition III. The simulation results show that the proposed algorithm improves the average F1-score achieved by the winner of the competition by 26.7% for latencies shorter than one second.
