Classification of Verbal and Quantitative Mental Tasks Using Phase Locking Values between EEG Signals

Atasoy, Huseyin; Yıldırım, Esen

doi:10.14257/ijsip.2016.9.7.34

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The above studies, based on different bio-signal data, report its accuracy at the level of even 94–97%. Other popular methods are Linear Discriminant Analysis (LDA) [ 21 ], k-Nearest Neighbours (kNN) [ 22 ], or Multilayer Perceptron (MLP) [ 23 ]. Multiclass problem studies can also be found.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

2021

View full text Add to dashboard Cite

The paper is focussed on the assessment of cognitive workload level using selected machine learning models. In the study, eye-tracking data were gathered from 29 healthy volunteers during examination with three versions of the computerised version of the digit symbol substitution test (DSST). Understanding cognitive workload is of great importance in analysing human mental fatigue and the performance of intellectual tasks. It is also essential in the context of explanation of the brain cognitive process. Eight three-class classification machine learning models were constructed and analysed. Furthermore, the technique of interpretable machine learning model was applied to obtain the measures of feature importance and its contribution to the brain cognitive functions. The measures allowed improving the quality of classification, simultaneously lowering the number of applied features to six or eight, depending on the model. Moreover, the applied method of explainable machine learning provided valuable insights into understanding the process accompanying various levels of cognitive workload. The main classification performance metrics, such as F1, recall, precision, accuracy, and the area under the Receiver operating characteristic curve (ROC AUC) were used in order to assess the quality of classification quantitatively. The best result obtained on the complete feature set was as high as 0.95 (F1); however, feature importance interpretation allowed increasing the result up to 0.97 with only seven of 20 features applied.

show abstract

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

2021

View full text Add to dashboard Cite

show abstract

“…The literature review shows that kNN methods were widely applied in cognitive workload classification in both two-and multiclass approach. The obtained accuracy varied in the range between 85% and 90% in case of subject dependent approach [36,44,47,51,53]. As for subject-independent approach, the results achieved with the use of kNN were in the range between 80% and 90%.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

et al. 2019

View full text Add to dashboard Cite

Evaluation of cognitive workload finds its application in many areas, from educational program assessment through professional driver health examination to monitoring the mental state of people carrying out jobs of high responsibility, such as pilots or airline traffic dispatchers. Estimation of multilevel cognitive workload is a task usually realized in a subject-dependent way, while the present research is focused on developing the procedure of subject-independent evaluation of cognitive workload level. The aim of the paper is to estimate cognitive workload level in accordance with subject-independent approach, applying classical machine learning methods combined with feature selection techniques. The procedure of data acquisition was based on registering the EEG signal of the person performing arithmetical tasks divided into six intervals of advancement. The analysis included the stages of preprocessing, feature extraction, and selection, while the final step covered multiclass classification performed with several models. The results discussed show high maximal accuracies achieved: ~91% for both the validation dataset and for the cross-validation approach for kNN model.

show abstract

Automated Classification of Cognitive Workload Levels Based on Psychophysiological and Behavioural Variables of Ex-Gaussian Distributional Features

et al. 2022

View full text Add to dashboard Cite

The study is focused on applying ex-Gaussian parameters of eye-tracking and cognitive measures in the classification process of cognitive workload level. A computerised version of the digit symbol substitution test has been developed in order to perform the case study. The dataset applied in the study is a collection of variables related to eye-tracking: saccades, fixations and blinks, as well as test-related variables including response time and correct response number. The application of ex-Gaussian modelling to all collected data was beneficial in the context of detection of dissimilarity in groups. An independent classification approach has been applied in the study. Several classical classification methods have been invoked in the process. The overall classification accuracy reached almost 96%. Furthermore, the interpretable machine learning model based on logistic regression was adapted in order to calculate the ranking of the most valuable features, which allowed us to examine their importance.

show abstract

Classification of Verbal and Quantitative Mental Tasks Using Phase Locking Values between EEG Signals

Cited by 3 publications

References 0 publications

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

Interpretable Machine Learning Models for Three-Way Classification of Cognitive Workload Levels for Eye-Tracking Features

A Three-Class Classification of Cognitive Workload Based on EEG Spectral Data

Automated Classification of Cognitive Workload Levels Based on Psychophysiological and Behavioural Variables of Ex-Gaussian Distributional Features

Contact Info

Product

Resources

About