# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
###### Abstract  
Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain-computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the \(\beta\) band. The results show that the characteristics of the \(\beta\) band are the most sensitive in EEG data under different mental workloads. BCI; EEG; feature selection; EEG band; Gini impurity; SVM +
Footnote †: journal: Medical  
the brain function and is closely related to neurological brain diseases such as cerebrovascular disease, epilepsy, and nervous system damage [5; 6]. Therefore, the analysis, classification, and recognition of EEG signals are of great significance for the pathological prediction, identification, and prevention of brain diseases. The EEG signal is very weak with small amplitude and big randomness. During the process of collecting signals, it is interfered by various noises, such as ocular electricity, myoelectricity, electrocardiogram, power frequency, and so on. Therefore, the collected EEG signals contain a variety of artifacts. As a common artifact, ocular electricity is much larger than the amplitude of EEG, and the energy is mainly concentrated in the low-frequency band. It seriously affects the delta wave (0.5-3 HZ) and theta wave in the basic rhythm wave of EEG (4-7 Hz). In order to eliminate the influence of ocular artifacts, in the clinic, doctors usually discard the EEG data segments containing ocular electrical interference, which may lead to the loss of some important EEG information. Obviously, this method is not advisable. Therefore, the removal of ocular artifacts has always been a very important research content for EEG signal preprocessing. The independent component analysis (ICA) method is a new signal decomposition technique developed in the 1990s and has been widely used in the field of signal processing. Because ocular electricity and brain electricity are generated by different sources and are independent of each other, ICA can be used to separate the ocular electricity from the EEG signal, thereby eliminating the astigmatism artifacts. In addition, [PERSON] et al. proposed a new approach for automated artifact elimination, which applies machine learning algorithms to ICA-based features. It provides an automatic, reliable, and real-time tool, which avoids the need for the time-consuming manual selection of ICs during artifact removal [7]. Relying on the deep learning ability of self-extracting the features of interest, [PERSON] et al. investigated the capabilities of convolutional neural networks (CNNs) for off-line, automatic artifact identification through ICs without feature selection [8]. [PERSON] et al. proposed an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electroocologram (EOG) recordings [9]. At present, researchers use algorithms for feature extraction and classification in various fields to study the feature extraction and classification of EEG signals.