# Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
## 1 Introduction  
Electroencephalography (EEG) data is composed of multi-channel bioelectric signals which are recorded by several electrodes and denote changes in bioelectric neural activity [1]. It is noninvasive and low cost and therefore used extensively in research. In the field of human psychology, EEG data is used to identify a persons' emotions, e.g., happiness, sadness, anger and shyness [2]. EEG data is also used in body control research to find changes in body movement (hands/feet-moving, blinking, etc.) and the corresponding brain activity [3; 4; 5; 6]. This paper focuses on people's cognitive workload since physiological mental fatigue is a significant issue that impacts health and lifestyle [7; 8]. In working environments, work stress can exceed the tolerance of people resulting in a decline in working performance. Having control of people's cognitive workload is important, particularly in activities which require a high level of concentration and attention. For example, engineers in a chemical factory must operate with high precision, and drivers need to maintain a healthy cognitive workload to prevent accidents [9]. Furthermore, workload estimations can be useful for many other applications, e.g., as quantitative measures for ergonomic and usability evaluations, or in medical applications [10]. Of the classical feature extraction methods, common space mode (CSP)-based methods are widely used to identify features [11; 12; 13], such as common sparse spectral pattern (CSSP) [14], sub-band common spatial pattern (SBCSP) [15], filter bank CSP (FBCSP) [16], discriminative CSP (DCSP) [17], and shrinkage regularized filter bank common spatial patterns (SR-FBCSP) [18] for binary EEG classification. Strong uncorrelated transform complex common spatial patterns (SUTCCSP) havebeen used to identify different responses of the mu and beta rhythms of EEG traces corresponding to motor imagery tasks [19]. In addition to CSP-based methods, independent component analysis (ICA) is used to filter EEG data [20]. [PERSON] used C4.5 algorithms to control a virtual simulator using EEG signals [21]. In the workload classification area, [PERSON] et al. used wireless EEG Signals to assess memory workload in the n-back task and achieved a general classification accuracy of over 80% [10]. [PERSON] et al. trained and evaluated a Support Vector Machine (SVM)-based workload classifier and achieved a multimodal recognition rate of 95% for the two-class visual task which drops to 73% for cognitive tasks. For the full three-class problem, [PERSON] et al. obtained recognition rates of 70% for the visual and 43% for the cognitive task [22]. [PERSON] et al. designed a SVM-based system for live recognition of mental workload and achieved an accuracy of 90.7 in the person-dependent evaluation while the accuracy dropped to 72.2 in the person independent evaluation [23]. Recently, [PERSON] used Fast Fourier Transformation (FFT) to convert EEG data into the frequency domain and map the 3D spatial positions of electrodes to 2D, according to the distribution of the electrodes. The 4-30 Hz frequency domain containing prime cortical activity is divided into theta (4-7 Hz), alpha (8-13 Hz) and beta (13-30 Hz) frequency bands, generating 3-channel spectral maps which are sent to deep neural networks. This method combines EEG data with deep neural networks and achieve an accuracy of 91.1% [24]. However, the data of the test and training set originated from one cohort of 13 subjects, and therefore if the model was to be used to classify their cognitive workload level of a new volunteer, the accuracy could decrease. Furthermore, FFT was performed on the time series for each trial to estimate the power spectrum of the signal. The sum of squared absolute values within \(\theta\), \(\alpha\) and \(\beta\) frequency bands was computed and used as separate measurements for each electrode, which causes different spectral ranges of spectral maps from different subjects different trails (the times subject finishing experimental tasks calls trails) [24].