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Abstract：
Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.
摘要：
基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)由于其高的信息传递率而受到广泛关注,精度高,丰富的指令集。然而,其识别方法的性能在很大程度上取决于受试者内部分类的校准数据量。一些研究使用深度学习(DL)算法进行学科间分类，这可以减少计算过程，但是与受试者内部分类相比，性能仍有很大的改进空间。为了解决这些问题,提出了一种基于软饱和非线性模块的高效SSVEP信号识别深度学习网络模型e-SSVEPNet。软饱和非线性模块在输出小于零时采用类似的指数计算方法,提高对噪声的鲁棒性。在SSVEP数据集的条件下，两个滑动时间窗口长度（1s和0.5s），和三个训练数据大小，本文对提出的网络模型进行了评估，并将其与其他传统和深度学习模型基线方法进行了比较。对非线性模块的实验结果进行了分类和比较。大量实验结果表明,所提出的网络在SSVEP数据集上具有最高的主体内分类平均准确率,提高了SSVEP信号分类识别的性能,并且在短信号下具有更高的解码精度，因此，它具有巨大的潜在能力，实现基于高速SSVEP的BCI。
