# A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data
## 1 Introduction
Other applied classification techniques applied to the problem of cognitive workload classification are artificial neural networks. In [24] [PERSON] et al. performed two-class classification based on Artificial Neural Networks (ANNs), achieving 86% of accuracy, whereas in [25], the same authors reported 98.5% accuracy achieved with the model built on EEG data in combination with heart rate, respiration, and eye movement measures. [PERSON] et al. applied ANNs [26] for distinguishing among three working-memory classes reaching averaged results of about 85%. However, these results were achieved for subject-specific analysis, where the classifier was trained for each subject and session. [PERSON] et al. in [27] presented a four-class linear classifier, obtaining average classification accuracy equal to 93.4%. However, these results were achieved also separately for each subject. Other classifications technique applied in the cognitive workload research cover SVM [28] and random forest classifiers [29]. ANNs were also used by [PERSON] et al. in [30], where the authors classified seven levels of cognitive workload with features extracted on the basis of wavelet entropy, achieving 83% classification accuracy. In [31], [PERSON] et al. reported 99% classification accuracy for two classes and 88% for four classes (both results achieved for eight subjects). In [32], [PERSON] et al. presented classification of four levels of cognitive workload with a linear probabilistic support vector machine (LIPSVM) at the level of 87%. [PERSON] et al. in [27] classified four cognitive levels achieving the accuracy equal to 92% using linear classification. The feature extraction procedure was based on statistical features. The main approach presented in the modern scientific literature is based on subject-specific cognitive workload [33], which is caused by high inter-subject variability of EEG signals. There are many examples of implementing subject-specific classification model for assessment of cognitive workload level in the literature [34; 35; 36]. Researchers also report higher classification performance for the within subject approach [37; 38]. Nevertheless, there is a clear need for developing a universal classifier that would be able to distinguish among several levels of cognitive workload of any person, regardless of external and internal conditions such as the age, time of day, or habits of an examined person. Using data from only one subject does not ensure good generalization, neither for other subjects nor even for other sessions of the same subject in other days and conditions [39]. Developing a subject-independent classifier requires analysis of broader datasets, which allows extracting more universal features, which in consequence can lead to better understanding of the processes occurring in human brain. As it was previously noted, most of available researches focus on a subject-dependent approach [40; 41; 42; 43; 44]. However, the need in subject-independent research has drawn the attention of researchers. Nevertheless, only a few publications presenting subject-independent approach based on classical EEG analysis [33; 45; 46] can be found. The literature review (Table 1) revealed that the many researches focus on only two classes of cognitive workload [40; 41; 42; 47]. However, multiclass approach, especially three-class [33; 48; 49], is gaining popularity. Although some researchers attempt to distinguish amongst more classes, the resulting classification accuracy is usually lower in the case. The literature overview shows that typical number of participants is close to 10 subjects. Eleven participants took part in the presented experiment. The main aim of the paper was to perform subject-independent evaluation of multilevel cognitive workload, ensuring the best accuracy using possibly lowest number of EEG-based features with the use of classical methods of machine learning, i.e., SVM, kNN, decision trees. Several models of multiclass classification were tested.