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Estimating the State of Pressing the Gas Pedal by Using driver EEG Data

机译:使用驱动器EEG数据估计压制气踏板的状态

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In the studies for safe driving, data from sensors and methods using psychological parameter values have been developed. Unlike these methods, electroencephalogram (EEG) signals and driving assistant development work can be detected as instant decisions on driving status. In this study, the acceleration and deceleration of the accelerator pedal were determined by using the EEG signals. For this estimation process, EEG data of 18 different drivers were analyzed by Welch method. The power density values of the delta, theta, alpha and beta frequency bands obtained as a result of the analysis were applied to the artificial neural network model as the features of the EEG signals. When the test data were applied to the trained network, an accuracy of 83% was estimated.
机译:在安全驾驶的研究中,已经开发了来自传感器和使用心理参数值的方法的数据。与这些方法不同,脑电图(EEG)信号和驾驶助理开发工作可以被检测为驾驶状态的即时决策。在该研究中,通过使用EEG信号确定加速器踏板的加速和减速。对于此估计过程,通过Welch方法分析了18个不同驱动器的EEG数据。作为分析结果获得的Δ,θ,alpha和β频带的功率密度值被应用于人工神经网络模型作为EEG信号的特征。当测试数据应用于培训的网络时,估计了83%的准确性。

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