# Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals +
## 2 Related Work  
The most generalized mechanisms to measure workload can be split into two main categories [9; 14; 15]: subjective measures based on the subject perception and objective scores based on physiological responses.  
On the one hand, subjective measures are still the most used to assess mental workload, the NASA Task Load Index (TLX) [16] being the most prominent test used to gain insights about the perceived workload levels while a subject works with various human-machine interface systems [17; 18]. This questionnaire measures the mental workload based on a weighted average of six sub-variables: mental demand, physical demand, temporal demand, performance, effort, and frustration. It is widely used in aviation to assess mental workload of pilots while interacting with plane controls [19; 20].  
On the other hand, physiological measures provide more reliable data of workload by measuring physiological dynamic changes which cannot be controlled consciously, so they have been becoming more popular among researchers in recent years [21; 22; 23]. The most common sensors/measurements used to record physiological data are: electrocardiogram (ECG) to register heart electrical activity, electromyograph to read skeletal muscles' electrical activity, electroencephalogram (EEG) to detect electrical activity in the brain, photoplethysmography to register volumetric changes in the blood flow, respiration rate sensors, electro-dermal activity (EDA) to read skin surface temperature, oxygen density in the blood in the brain, and eye movement trackers, among others [24]. TLX surveys allow one to assess the perceived workload [16], but it is highly subjective. However, physiological data occur spontaneously, and together with TLXs, provide more reliable information [9; 17; 21].  
The combination of several physiological sensors to classify workload states gives better results than using a single one. The approach proposed in [25] combines EEG, ECG, and electrooculography (EOG); and results show the best predictive power for their combination (80%) rather than the analysis of each one independently (70%). In addition, the study in [10] reports an accuracy average of 85.2 (\(\pm\)4.3%) combining EEG, ECG, respiration rate, and EDA to classify four mental states. The work in [26] still shows better results combining EEG, ECG, and EDA than using only EEG signals from classifying four mental states, although results from the single sensor are promising (86.66%).  
Deep learning (DL) approaches are gaining ground over more classical machine learning techniques due to their ability to automatically extract features [24; 27; 28].  
The application fields fall into five general groups: emotion recognition [29], motor imagery [30], seizure detection [31], sleep scoring [32], and mental workload. [PERSON] et al. [33] combined functional Near Infrared Spectroscopy (fNIRS) and adapted a CNN architecture to allow fNIRS-EEG input to the CNN with promising results (89/5 of correct classification). The study in [6] proposed a concatenated structure of deep recurrent and 3D convolutional neural networks to combine both raw and spectral EEG data and assess two degrees of mental workload, reporting an average accuracy of 88.9% in a cross-task assessment. In the same fashion, [PERSON] et al. [34] proposed a LSTM based temporal attention technique to simultaneously extract EEG features containing both local and global structure information, obtaining an accuracy of 90.8% on their own dataset. [PERSON] et al. presented a new framework using the grey wolf optimizer algorithm and deep BLSTM-LSTM neural model for estimating different levels of mental workload, achieving 86.33% and 82.57% classification accuracy for \"No task\" and \"multitasking activity\" experiments, respectively. None of them transfer learning to another type of task.