基于稳态视觉诱发电位的脑机界面时间和频率深度频率识别

Ebru Sayilgan, Yilmaz Kemal Yuce, Y. Isler
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引用次数: 10

摘要

基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)系统已加速应用于从娱乐到康复的不同应用领域,如临床神经科学、认知和工程应用研究。在各种脑电图范式中,基于ssvep的脑机接口系统具有系统结构简单、训练时间短或无需训练、时间分辨率高、信息传输率高、与其他方法相比经济实惠等优点,使中风患者能够轻松地与外界进行交流。基于ssvep的脑机接口使用以不同频率闪烁的多个视觉刺激来生成不同的命令。在本文中,我们比较了在七个不同频率下闪烁的二进制命令组合的分类器性能,以确定使用时间和频谱方法的频率对具有最高性能。对于SSVEP频率识别,从SSVEP信号中提取了25个信号的时间变化特征和15个基于频率的特征向量。这些特征向量被应用于七种著名的机器学习算法(决策树、判别分析、逻辑回归、朴素贝叶斯、支持向量机、最近邻和集成学习)的输入。总之,在这2520个不同的运行中,我们在7.5 - 10个频率对中实现了100%的准确率,我们发现最成功的分类器是集成学习分类器。这些方法的结合导致了适当的详细和比较分析,代表了经典方法的鲁棒性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frequency Recognition from Temporal and Frequency Depth of the Brain-Computer Interface based on Steady-State Visual Evoked Potentials
Brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) have been acceleratingly used in different application areas from entertainment to rehabilitation, like clinical neuroscience, cognitive, and use of engineering researches. Of various electroencephalography paradigms, SSVEP-based BCI systems enable apoplectic people to communicate with outside world easily, due to their simple system structure, short or no training time, high temporal resolution, high information transfer rate, and affordable by comparing to other methods. SSVEP-based BCIs use multiple visual stimuli flickering at different frequencies to generate distinct commands. In this paper, we compared the classifier performances of combinations of binary commands flickering at seven different frequencies to determine which frequency pair gives the highest performance using temporal and spectral methods. For SSVEP frequency recognition, in total 25 temporal change characteristics of the signals and 15 frequency-based feature vectors extracted from the SSVEP signal. These feature vectors were applied to the input of seven well-known machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbour, and Ensemble Learning). In conclusion, we achieved 100% accuracy in 7.5 - 10 frequency pairs among these 2,520 distinct runs and we found that the most successful classifier is the Ensemble Learning classifier. The combination of these methods leads to an appropriate detailed and comparative analysis that represents the robustness and effectiveness of classical approaches.
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