Seifzadeh Sahar, Rezaei Mohammad, Faez Karim, Amiri Mahmood
Department of Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
J Med Signals Sens. 2017 Apr-Jun;7(2):80-85.
Brain-computer interfaces enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. One of the most challenging issues in this regard is the balance between the accuracy of brain signals from patients and the speed of interpreting them into machine language. The main objective of this paper is to analyze different approaches to achieve the balance more quickly and in a better way. To reduce the ocular artifacts, the symmetric prewhitening independent component analysis (ICA) algorithm has been evaluated, which has the lowest runtime and lowest signal-to-interference (SIR) index, without destroying the original signal. After quick elimination of all undesirable signals, two successful feature extractors - the log-band power algorithm and common spatial patterns (CSPs) - are used to extract features. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during the imagination of the tongue, feet, and left-right-hand movement. Finally, three well-known classifiers are evaluated, where the ridge regression classifier and CSPs as feature extractor have the highest accuracy classification rate about 83.06% with a standard deviation of 1.22%, counterposing the recent studies.
脑机接口使用户能够通过头皮上的脑电图（EEG）活动或大脑内部的单神经元活动来控制设备。在这方面，最具挑战性的问题之一是患者脑信号的准确性与将其转换为机器语言的速度之间的平衡。本文的主要目标是分析以更快、更好的方式实现这种平衡的不同方法。为了减少眼电伪迹，对对称白化独立成分分析（ICA）算法进行了评估，该算法运行时间最短，信号干扰比（SIR）指数最低，且不会破坏原始信号。在快速消除所有不需要的信号后，使用了两种成功的特征提取器——对数带功率算法和共同空间模式（CSP）——来提取特征。重点在于识别代表舌头、脚部以及左右手部运动想象期间记录的脑电图试验的特征集的判别属性。最后，对三种著名的分类器进行了评估，其中岭回归分类器和作为特征提取器的CSP在与近期研究对比时，具有最高的准确率，约为83.06%，标准差为1.22%。
