Steady-state visual evoked potential (SSVEP) based brain–computer interfaces have been widely studied because these systems have potential to restore capabilities of communication and control of disable people. Identifying target frequency using SSVEP signals is still a great challenge due to the poor signal-to-noise ratio of these signals. Commonly, this task is carried out with detection algorithms such as bank of frequency-selective filters and canonical correlation analysis. This work proposes a novel method for the detection of SSVEP that combines the empirical mode decomposition (EMD) and a power spectral peak analysis (PSPA). The proposed EMD+PSPA method was evaluated with two EEG datasets, and was compared with the widely used FB and CCA. The first dataset is freely available and consists of three flickering light sources; the second dataset was constructed and consists of six flickering light sources. The results showed that proposed method was able to detect SSVEP with high accuracy (93.67 ± 9.97 and 78.19 ± 23.20 for the two datasets). Furthermore, the detection accuracy results achieved with the first dataset showed that EMD+PSPA provided the highest detection accuracy (DA) in the largest number of participants (three out of five), and that the average DA across all participant was 93.67 ± 9.97 which is 7% and 4% more than the average DA achieved with FB and CCA, respectively.

基于稳态视觉诱发电位（SSVEP）的脑机接口已经得到广泛研究，因为这些系统具有恢复通讯能力和控制残疾人的潜力。由于这些信号的差的信噪比，使用SSVEP信号识别目标频率仍然是一个巨大的挑战。通常，此任务通过检测算法来执行，例如频率选择滤波器组和规范相关分析。这项工作提出了一种新的SSVEP检测方法，该方法结合了经验模式分解（EMD）和功率谱峰值分析（PSPA）。拟议的EMD + PSPA方法通过两个EEG数据集进行了评估，并与广泛使用的FB和CCA进行了比较。第一个数据集是免费提供的，由三个闪烁的光源组成。第二个数据集已构建，由六个闪烁的光源组成。结果表明，所提出的方法能够高精度检测SSVEP（两个数据集的检出率分别为93.67±9.97和78.19±23.20）。此外，使用第一个数据集获得的检测准确性结果表明，EMD + PSPA在最大数量的参与者（五分之三）中提供了最高的检测准确性（DA），并且所有参与者的平均DA为93.67±9.97，其中分别比FB和CCA获得的平均DA高出7％和4％。