# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 2 Materials and Methods
### EEG Feature Extraction  
This section employs filter bank common spatial patterns (FBCSPs) for EEG feature extraction. FBCSP filters the signals across various frequency bands, transforming the high-dimensional time-domain EEG data into a low-dimensional space. This approach maximizes the class covariance, enhancing classification performance. The implementation process of CSP [32] is summarized as follows.  
The normalized spatial covariance \(C\) of the EEG data (\(D\in RM\times L\)) is defined by Equation (1):  
\[C=\frac{DD^{\rm T}}{trace(DD^{\rm T})}, \tag{1}\]  
where \(M\) indicates the number of EEG channels, \(L\) represents the total number of samples, and T signifies the matrix transpose. For two types of EEG data, the spatial covariance matrices \(C_{1}\) and \(C_{2}\) are computed by averaging the spatial covariance \(C\) of each type of EEG data.  
The combined covariance matrix \(C_{z}\) is then constructed as shown in Equation (2):  
\[C_{z}=C_{1}+C_{2}, \tag{2}\]  
where the covariance matrix \(C_{z}\) is decomposed to yield the eigenvalues matrix \(A\) and the eigenvector matrix \(F_{z}\), as shown in Equation (3):  
\[C_{z}=F_{z}AF_{z}^{\rm T}, \tag{3}\]  
where the whitening matrix \(P\) is derived through the whitening transformation, as shown in Equation (4):  
\[P=\sqrt{A^{-1}}F_{z}^{\rm T}. \tag{4}\]  
The matrices \(S_{1}\) and \(S_{2}\) are calculated as shown in Equations (5) and (6):  
\[S_{1}=PC_{1}P^{\rm T}=UE_{1}U^{\rm T}, \tag{5}\]\[S_{2}=PC_{2}P^{\rm T}=UE_{2}U^{\rm T}, \tag{6}\]  
where the eigenvectors of matrices \(S_{1}\) and \(S_{2}\) are identical, and \(E_{1}\) and \(E_{2}\) satisfy \(E_{1}+E_{2}=I\), which is the identity matrix. \(U\) is the matrix of eigenvectors in descending order.  
The projection matrix \(W\) is then obtained by arranging the eigenvalues in descending order, as shown in Equation (7):  
\[W=U^{\rm T}P\in R^{M\times M}. \tag{7}\]  
After the EEG data is projected through CSP, the resulting matrix \(V\) is given by Equation (8):  
\[V=W\cdot D, \tag{8}\]  
where \(D\) is the preprocessed EEG data. The mean square error of the features is calculated, followed by a logarithmic transformation to obtain the EEG feature vector, as shown in Equation (9):  
\[f_{t}=\log(\frac{\operatorname{var}(V_{t})}{\sum_{j=1}^{2m}\operatorname{ var}(V_{j})}),t=1,2,\ldots,2m. \tag{9}\]  
In a four-class classification problem, feature extraction involves treating one class as a distinct category while combining the remaining three classes into a single group. This approach transforms the original problem into four binary classification tasks, each corresponding to a unique spatial filter. These filters are applied to EEG signals, producing four sets of features, which are then aggregated to form the final extracted features. The feature extraction process of FBCSP is shown in Figure 5.  
In FBCSP feature extraction, a zero-phase FIR filter is designed to process EEG signals within the 4-30 Hz frequency range, focusing on the prominent \(\theta\), \(\alpha\), and \(\beta\) waves. The 4-30 Hz range is divided into multiple segments: 4-8, 6-10, 8-12, 10-14, 12-16, 14-18, 16-20, 18-22, 20-24, 22-26, 24-28, and 26-30 Hz. CSP feature extraction is then applied to each filtered segment, generating 24 features per segment, resulting in a total of 288 features per sample.