# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Data Acquisition and Pre-Processing  
#### 3.3.1 EEG Data  
The EEG data were captured using the EMOTIV EPOC X device, which has a resolution of 14 bits and a sampling rate of 128 Hertz (Hz). This device was equipped with 14 channels, and the sensor placements adhered to the international 10-20 system. The EMOTIV Pro software (version 2.6.4) conducted real-time frequency analysis on each channel and also allowed for the analysis of recorded data. It visually presented the power in different frequency bands for each selected channel. The power changes of each frequency were displayed in decibels (dB). The power densities obtained for each frequency band of the EEG signals, which were divided into sub-bands using FFT (fast Fourier transform), could be observed in the \"Band Power\" graph located at the bottom of the screen. The frequency ranges for different bands in the software were as follows: theta band (4-8 Hz), alpha band (8-12 Hz), gamma band (25-45 Hz), low beta (12-16 Hz),  
Figure 3: N-back task levels: (**a**) 0-back, (**b**) 1-back, (**c**) 2-back, (**d**) 3-back. and high beta (16-25 Hz). The variables analyzed in the study were aligned with these band intervals. The \"FFT/Band Power\" data were transferred to an external environment, allowing access to power data for five frequency bands per channel. Unlike raw EEG data, which had a frequency of 128 Hz, the band power data were transferred with a frequency of 8 Hz. For EEG recordings with a quality measurement above 75%, the average values of band power variables provided by the software were obtained based on stimuli. The variable \"AF3.Theta\" represented the average theta power from the AF3 channel, while \"AF3.HighBeta\" represented the average high beta power from the same channel. A total of 70 EEG variables, representing power in five frequency bands (theta, alpha, low beta, high beta, gamma), were used for 14 channels. Additionally, based on previous studies indicating a positive relationship between the theta/alpha power ratio and difficulty level, 14 more variables corresponding to this ratio were included in the analysis for each channel [26; 40]. For example, the variable indicating the theta/alpha power ratio for the AF3 channel was labeled as \"AF3.ThetaAlpha\". The unit of measurement for EEG variables was given as dB. The raw EEG signals underwent pre-processing to remove noise using the EEGLAB toolbox [41]. Following the pre-processing, the STUDY functionality of EEGLAB was utilized to investigate the EEG mechanisms across subjects under different task conditions. This approach aimed to complement the statistical findings obtained from SPSS and provide a visual representation of the group analysis of the raw EEG data. The raw EEG data, acquired through EMOTIV Pro 3 software, were converted into a.set file and imported into the program along with the.ced file containing the coordinate data for the 14 electrode channels. Filtering and data pre-processing techniques were applied to eliminate noise from the raw data. The recommended procedure, as suggested by the developers of EEGLAB and employed in various studies [23; 26; 42; 43], was followed. Noise is an undesirable distortion that occurs in the EEG signal, compromising the reliability of the obtained results. In order to eliminate this noise, a filtering process was conducted during the pre-processing stage of the study. A finite impulse response (FIR) filter was utilized to perform band-pass filtering, specifically ranging from 1 to 50 Hz. Additionally, to address the interference caused by line noise in the raw EEG signal [44], another filtering procedure was carried out using the CleanLine plugin in EEGLAB. Subsequently, the \"Independent Component Analysis--ICA\" technique was employed to separate the data into independent components, enabling the identification and removal of noise. This method was implemented in EEGLAB through the utilization of the \"runica\" function.