Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals

In this study, we focused on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from resting-state electroencephalographic recordings of patients who were diagnosed with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing the fuzzy measure densities. The dataset applied in the study was a collection of variables characterizing the organization of the neural networks computed using the minimum spanning tree (MST) algorithms obtained from signal-spaced functional connectivity indicators and calculated separately for predefined frequency bands using classical linear Granger causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained using numerous generalizations of the Choquet integral and other aggregation functions, which were tested to find the most appropriate ones. The obtained results demonstrate that the classification accuracy can be increased by 1.81% using the extended versions of the Choquet integral called in the literature, namely, generalized Choquet integral or pre-aggregation operators.

On the Improvement of Eye Tracking-Based Cognitive Workload Estimation Using Aggregation Functions

Cognitive workload, being a quantitative measure of mental effort, draws significant interest of researchers, as it allows to monitor the state of mental fatigue. Estimation of cognitive workload becomes especially important for job positions requiring outstanding engagement and responsibility, e.g., air-traffic dispatchers, pilots, car or train drivers. Cognitive workload estimation finds its applications also in the field of education material preparation. It allows to monitor the difficulty degree for specific tasks enabling to adjust the level of education materials to typical abilities of students. In this study, we present the results of research conducted with the goal of examining the influence of various fuzzy or non-fuzzy aggregation functions upon the quality of cognitive workload estimation. Various classic machine learning models were successfully applied to the problem. The results of extensive in-depth experiments with over 2000 aggregation operators shows the applicability of the approach based on the aggregation functions. Moreover, the approach based on aggregation process allows for further improvement of classification results. A wide range of aggregation functions is considered and the results suggest that the combination of classical machine learning models and aggregation methods allows to achieve high quality of cognitive workload level recognition preserving low computational cost.

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

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.

Cognitive inconsistency in bipolar patients is determined by increased intra-individual variability in initial phase of task performance

BACKGROUND

Bipolar patients show high intra-individual variability during cognitive processing. However, it is not known whether there are a specific fluctuations of variability contributing to the overall high cognitive inconsistency. The objective was to compare dynamic profiles of patients and healthy controls to identify hypothetical differences and their associations with overall variability and processing speed.

METHODS

Changes of reaction times iSD during processing speed test performance over time was measured by dividing the iSD for whole task into four consecutive parts. Motor speed and cognitive effort were controlled.

RESULTS

Patients with BD exhibited significantly lower results regarding processing speed and higher intra-individual variability comparing with HC. The profile of intra-individual variability changes over time of performance was significantly different in BD versus HC groups: F(3, 207)=8.60, p<0.0001, η_p²=0.11. iSD of BD patients in the initial phase of performance was three times higher than in the last. There was no significant differences between four intervals in HC group. Inter-group difference in the initial part of the profiles was significant also after controlling for several cognitive and clinical variables.

LIMITATIONS

Applied computer version of Cognitive Speed Test was relatively new and, thus, replication studies are needed. Effect seen in the present study is driven mainly by the BD type I.

CONCLUSIONS

Patients with BD exhibits problems with setting a stimulus-response association in starting phase of cognitive processing. This deficit may negatively interfere with the other cognitive functions, decreasing level of psychosocial functioning, therefore should be explored in future studies.