# A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data
## 1 Introduction  
Cognitive workload is defined as a quantitative measure of the amount of mental effort needed to perform a task [1]. In our research, we distinguish the terms \"cognitive workload\" and \"mental fatigue\", whereas mental fatigue is defined as a psychological state related to the loss of work capacity [2] \"triggered by prolonged periods of demanding cognitive activity\" [3]. Assessment of human mental effort is an important, but not trivial task. Research on cognitive workload gives an opportunity to understand the process of mental fatigue, including analyzing the influence of different complexity tasks on mental effort and concentration level. What is more, estimation of mental effort may be helpful in adjusting learning techniques and cognitive sources, as well as in understanding the human performance of different level tasks and information processing capabilities. Evaluation of cognitive workload used to be performed with subjective measures, for example, based on interviews or questionnaires, where participants assessed the amount of mental effort they put into a task [4]. These approaches, however, depend on the subjective opinion of participants, gathered on the basis of psychophysiological measurement [5; 6] or subjective scales [7], and usually do not assure reliable, comparable results. An alternative approach covers monitoring the cognitive workload changes with neurophysiological measures such as Galvanic Skin Response (GSR) [8], electroencephalogram (EEG), pupillometry, or cardiogram (ECG) [9; 10]. Brain activity measurement was proven to be effective in visualizing cognitive state changes. Widely used approach of cognitive workload level estimation takes advantage of the EEG signals [11]. One of the most popular types of features extracted from EEG signals is power spectrum [12]. According to [PERSON] [13] and [PERSON] [14], theta (\(\theta\)) and alpha (\(\alpha\)) oscillations are sensitive to task difficulty. More recent results show an increase of theta oscillations in the frontal region and decrease in parietal region [15]. Alpha and theta variations were also analyzed by [PERSON] et al. in their paper [16], where EEG, GSR, and breathing rate were combined to estimate mental fatigue among drivers in a driving simulator performing a secondary cognitive task. Frontal theta power was also proved to be positively related to working memory engagement and attentional control in a study of different difficulty tasks combined with practice [17; 18]. [PERSON] et al. [15] proposed the EEG-based measure of the overall brain load. It was determined as a \(\theta\)(Fz)/\(\alpha\)(Pz) ratio, where Fz and Pz are electrodes. In [19] the mental state of individuals was analyzed on the basis of changes in EEG power spectral density, especially in the theta and alpha bands, where the average classification accuracy reached, respectively, 79% and 78%. The classifier applied was based on SVM. The results show that both the alpha and theta powers decrease in central and posterior regions with the increase of the level of difficulty. [PERSON] et al. examined the mental workload during a novel and complex cognitive-motor task with two levels of difficulty, realized by participants over several days [20]. Greater engagement of working memory was correlated with the theta band synchrony. The level of task difficulty was positively related to alpha desynchrony as well as to the theta/alpha ratio. No interaction was found between task difficulty and particular practice days for both the theta and alpha power. Other approaches propose cognitive workload measurement based on Event Related Potentials (ERPs) and the P300 component [21] or on spatial filters, such as Common Spatial Pattern (CSP) [22], [PERSON] et al. presented in their paper [23] a modified version of the LDA algorithm applied to assess drivers' workload based on EEG alpha and theta bands and Eye-Tracking (ET) data. Authors reached classification performance of about 75%.