脑-机接口
计算机科学
线性判别分析
特征提取
支持向量机
人工智能
模式识别(心理学)
特征选择
极限学习机
稳健性(进化)
运动表象
信号处理
过滤器组
脑电图
机器学习
语音识别
滤波器(信号处理)
人工神经网络
数字信号处理
计算机视觉
计算机硬件
生物化学
化学
心理学
精神科
基因
作者
Sarah N. Carvalho,Thiago Bulhões da Silva Costa,Luisa Fernanda Suárez Uribe,Diogo C. Soriano,Glauco Ferreira Gazel Yared,Luis Cláudius Coradine,Romis Attux
标识
DOI:10.1016/j.bspc.2015.05.008
摘要
Brain–computer interface (BCI) systems based on electroencephalography have been increasingly used in different contexts, engendering applications from entertainment to rehabilitation in a non-invasive framework. In this study, we perform a comparative analysis of different signal processing techniques for each BCI system stage concerning steady state visually evoked potentials (SSVEP), which includes: (1) feature extraction performed by different spectral methods (bank of filters, Welch's method and the magnitude of the short-time Fourier transform); (2) feature selection by means of an incremental wrapper, a filter using Pearson's method and a cluster measure based on the Davies–Bouldin index, in addition to a scenario with no selection strategy; (3) classification schemes using linear discriminant analysis (LDA), support vector machines (SVM) and extreme learning machines (ELM). The combination of such methodologies leads to a representative and helpful comparative overview of robustness and efficiency of classical strategies, in addition to the characterization of a relatively new classification approach (defined by ELM) applied to the BCI-SSVEP systems.
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