Electroencephalogram (EEG) is the recording which is the result due to the activity of bio-electrical signals that is acquired from electrodes placed on the scalp. In Electroencephalogram signal(EEG) recordings, the signals obtained are contaminated predominantly by the Electrooculogram(EOG) signal. Since this artifact has higher magnitude compared to EEG signals, these noise signals have to be removed in order to have a better understanding regarding the functioning of a human brain for applications such as medical diagnosis. This paper proposes an idea of using Independent Component Analysis(ICA) along with cross-correlation to de-noise EEG signal. This is done by selecting the component based on the cross-correlation coefficient with a threshold value and reducing its effect instead of zeroing it out completely, thus reducing the information loss. The results of the recorded data show that this algorithm can eliminate the EOG signal artifact with little loss in EEG data. The denoising is verified by an increase in SNR value and the decrease in cross-correlation coefficient value. The denoised signals are used to train an Artificial Neural Network(ANN) which would examine the features of the input EEG signal and predict the stress levels of the individual.
翻译:电脑图(EEG)是使用从头皮上放置的电极获得的生物电信号活动产生的记录。在电脑图信号记录中,获得的信号主要受到电离剖面信号的污染。由于这种工艺品比EEEG信号规模更大,必须去除这些噪音信号,以便更好地了解人体大脑在医疗诊断等应用方面的功能。本文件提出使用独立组件分析(ICA)以及跨镜与降声EEEEG信号的交叉关系的想法。这是通过根据具有临界值的交叉孔关系系数选择组件来完成的,并减少其效果,而不是将其完全除去,从而减少信息损失。记录的数据结果表明,这种算法可以消除EOG信号制品,而不会在EEG数据中损失多少。通过SNR值的提高和跨镜系系数值的降低来验证这种消音化作用。去除信号的信号用于对EGA预测的单个电磁度进行测试。