# Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
## 3 Parallel Mechanism of Spectral Feature-Enhanced Maps  
### The Structure of Parallel Mechanism  
Figure 2 shows the overview of our approach. EEG raw data from different channels is converted into the frequency domain using FFT and combined with the electrode space information, producing \(\theta\), \(\alpha\) and \(\beta\) spectral maps [24]. After performing the feature enhancement process, the overall structure features are determined as \(\theta\)', \(\alpha\)' and \(\beta\)' spectral-enhanced maps. \(\theta\), \(\theta\)', \(\alpha\), \(\alpha\)', \(\beta\) and \(\beta\)' form 6-channel spectral maps, which is the establishment of a parallel mechanism. Finally, PMSFE maps are sent to several milestone neural networks, e.g., AlexNet, VGGNet, ResNet, DenseNet to test for accuracy and generalization [25; 26; 31; 32; 33; 34]. Figure 1: The mean topological spectral maps of \(\theta\), \(\alpha\) and \(\beta\) at a 4 classes load level. Maps in the first line show the spectral changes in the \(\theta\) frequency band under different load level. Maps in the second line show the spectral changes in the \(\alpha\) frequency band under different load levels. Maps in the third line show the spectral changes in the \(\beta\) frequency band under different load levels. All values have been subtracted from the average spectral value of the whole dataset. don't have a large fluctuation range of spectral power values. In the four cognitive load levels, pictures with unremarkable features are very common, indicating the 2670 trails range should be considered and every trail needs to be enhanced to fully identify the structural information. If these spectral maps are only regulated in the whole standard, the structure information of maps with unremarkable features would be compressed. Since EEG data is very sensitive, any slight body movement or distraction of mind could result in significant changes in the spectral map and influence the final result. Therefore, in addition to the power information of each electrode frequency domain, the whole structure of the spectral map also needs to be considered. An individual's cognitive workload level is related to its spectral power and also the overall spatial distribution. Spectral maps that have not been enhanced on a self-based basis can weaken the expression of the structure information in deep neural networks. The details of enhancing the structure features of spectral map are described as follows. After the \(\theta_{n}\), \(\alpha_{n}\) and \(\beta_{n}\) spectral power values have been subtracted from the overall average, Equation (5) is used to project all the \(\theta_{n}\), \(\alpha_{n}\) and \(\beta_{n}\) into a range from 0 to 1, and \(\theta_{n}^{*}\), \(\alpha_{n}^{*}\) and \(\beta_{n}^{*}\) are thus obtained:  
\[A_{n}^{i*}=\frac{1}{1+e^{-0.15\cdot A_{n}^{i}}} \tag{5}\]  
Since the spectral power is calculated by the sum of squared absolute values within each of the \(\theta_{n}\), \(\alpha_{n}\) and \(\beta_{n}\) frequency bands, Equation (5) could weaken the impact of the square operation and avoid the interference from abnormal large values. Equation (8) is used to widen the range of minimum and maximum values:  
\[Max(A_{n}^{i*})=Max(A_{\theta_{n}^{*}}^{1*},A_{\theta_{n}^{*}}^{2*}\cdots,A_{ \theta_{n}^{*}}^{i*}\cdots,A_{\theta_{n}^{*}}^{64*},A_{\alpha_{n}^{*}}^{1*},A_{ \alpha_{n}^{*}}^{2*}\cdots,A_{\alpha_{n}^{*}}^{i*}\cdots,A_{\beta_{n}^{*}}^{64* },A_{\beta_{n}^{*}}^{1*},A_{\beta_{n}^{*}}^{2*}\cdots,A_{\beta_{n}^{*}}^{i*} \cdots,A_{\beta_{n}^{*}}^{64*}) \tag{6}\]  
Figure 3: Visualization of examples from four different workload levels. (**a–d**) show some samples of maps with obvious features in four classes and the range of spectral value are given. (**e–h**) show some samples of maps with unobvious features in four classes and the range of spectral value are given. All these values have been subtracted from the average spectral value of the whole dataset.