Taghizadeh-Sarabi Mitra, Niksirat Kavous Salehzadeh, Khanmohammadi Sohrab, Nazari Mohammadali
Department of Mechatronics Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Department of Control Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Springerplus. 2013 Dec 10;2:662. doi: 10.1186/2193-1801-2-662. eCollection 2013.
In this article a quantitative analysis was devised assessing driver's cognition responses by exploring the neurobiological information underlying electroencephalographic (EEG) brain signals in a left and right turning experiment on simulator environment. Driving brain signals have been collected by a 19-channel electroencephalogram recording system. The driving pathway has been selected with no obstacles, a set of indicators are used to inform the subjects when they had to turn left or right by means of keyboard left and right arrows. Subsequently in order to remove artifacts, preprocessing is performed on data to achieve high accuracy. Features of signals are extracted by using Fast Fourier Transform (FFT). Absolute power of FFT is used as a basic feature. Scalar Feature selection method is applied to reduce feature dimension. Thereafter dimension-reduced features are fed to Hopfield Neural Network (HNN) recognizing different brain potentials stimulated by turning to left and right. The performances of HNN are evaluated by considering five conditions; before feature extraction, after feature extraction, before reduction of features, after analyzing reduced features and finally subject-wise Hopfield performances respectively. An increase occurred in each level and continued until it has reached its highest 97.6% of accuracy on last condition.
在本文中，设计了一种定量分析方法，通过在模拟器环境下的左右转弯实验中探索脑电图（EEG）脑信号背后的神经生物学信息，来评估驾驶员的认知反应。驾驶脑信号由一个19通道脑电图记录系统采集。驾驶路径选择为无障碍物，通过键盘左右箭头向受试者发出向左或向右转的指令。随后，为了去除伪迹，对数据进行预处理以实现高精度。使用快速傅里叶变换（FFT）提取信号特征。FFT的绝对功率用作基本特征。应用标量特征选择方法来减少特征维度。此后，将降维后的特征输入霍普菲尔德神经网络（HNN），以识别由向左和向右转所激发的不同脑电位。通过考虑五个条件来评估HNN的性能；分别为特征提取前、特征提取后、特征降维前、分析降维特征后以及最终按受试者的霍普菲尔德性能。在每个阶段准确率都有所提高，直到在最后一个条件下达到最高的97.6%的准确率。
