# Mental Workload Classification Method Based on EEG Independent Component Features
## 2 Materials and Methods
### Mental Workload Classification Method Based on EEG ICs Features  
#### 2.2.1 BSS of EEG Signals  
The EEG signals are usually collected by non-invasive measurement technology, and its electrode sensors are placed on the head surface. In a human thought process, as shown in Figure 3(a) [24], many neuron sources are activated and separately generate brain activity signals, (\(S_{1}\),\(S_{2}\),... \(S_{n}\)) [25]. But the electrode sensors actually measure their mixed signals. The measured mixed signals (\(S_{1}\),\(S_{2}\),...,\(S_{m}\)) are not suitable to distinguish the EEG features and should be separated into their independent pure signals. This mixed effect depends on the distances from neuron sources to electrode sensors, \(a_{ij}\), (\(i\) = 1,2,..., \(m\); \(j\) = 1,2,..., \(n\)) [26]. There will be a fixed mix feature for the same person wearing the same EEG cap. The problem in separating mixed EEG signals is similar to the BSS of mixed speech signals, as shown in Figure 3b. Both separate their mixed signals in order to obtain independent pure signals for further analysis.  
The independent component analysis (ICA) [27] is often used to separate the mixed speech signals in order to obtain their ICs. Therefore, this paper borrows the idea of BSS to realize the mental workload classification for vision and operational tasks. The method includes four steps: filtering the measured EEG signals; obtaining EEG ICs with ICA; extracting the energy features of ICs; and classifying them.  
This method firstly obtains the pure signals of EEG, and then classifies the mental workload by analyzing the ICs features of EEG directly. The BSS process of EEG signals is as follows:  
Figure 3: Similarity analysis of signal mixing process of electroencephalogram (EEG) and speech. (**a**) Signal mixing process of EEG; (**b**) Signal mixing process of speech.  
In Figure 3a, m-dimensional observation vector, \(x_{1}\), \(x_{2}\), \(\ldots\)\(x_{m}\), is a linear combination of n-dimensional ICs, \(s_{1}\), \(s_{2}\), \(\ldots\)\(s_{n}\), and can be expressed as:  
\[x_{j}=a_{j1}s_{1}+a_{j2}s_{2}+\ldots+a_{jn}s_{n},j=1,2,3\ldots,m \tag{1}\]  
It can be formulated as:  
\[X=AS \tag{2}\]  
where \(S=[s_{1},s_{2},\ldots\)\(s_{n}]^{T}\) is a n-dimensional vector of statistically independent sources, in this paper \(S\) is a set of ICs; \(X=[x_{1},\)\(x_{2},\ldots\)\(x_{m}]^{T}\) is m-dimensional vector of observed data; \(A\) is a mixing matrix.  
These independent sources are mixed through the m \(\times\) n mixing matrix, \(A\). \(A\) represents the mixed situation of signals. In our study, the source, \(S\), and the mixing matrix, \(A\), are unknown. Here, ICA used the observed random vector data, \(X\), to estimate \(S\) using optimization and iterative methods [28; 29; 30]. This paper selected FastICA [31] to estimate \(S\).  
Each EEG IC is equivalent to a signal generated by a neuron source or several nearby neurons. It is a great advantage to analyze the EEG ICs rather than the mixed EEG signals because the features of mixed EEG are vague. EEG signals can actually be analogized to multi-dimensional speech signals. It is generally known that it is easy to identify the content of the speech signals for a single sound source. But for mixed sound sources, multiple speech signals are aliased, which makes the semantics ambiguous. It is very difficult to identify them unless using the BSS of speech signals. Borrowing the idea of BSS, this paper presents a mental workload classification method based on EEG ICs features.  
For the presented method, the number of selected ICs was 30 in this paper, and is the same as the number of EEG signal channels considering the following reasons: (1) The dimension of the ICA output variable, \(S\), cannot exceed the dimension of the ICA input variable \(X\)[30]; and (2) by comparing the impact of different dimensions of \(S\) on its classification accuracy, we find that the classification accuracy is highest when the dimension of \(S\) is 30, which will be shown in Section 3.4.