Monika Kaczorowska

An analysis of eye-tracking and electroencephalography data for cognitive load measurement during arithmetic tasks

The paper presents multiple features analysis of cognitive load case study. The set of features applied in the research covers response times, committed errors, EEG spectral data as well as pupillometry and eye-tracking (ET) data including fixations, saccades and blinks. The experiment took the form of eleven intervals: six containing arithmetic tasks and five breaks. Two correlation analyses were performed. The first one aimed in finding correlation between cognitive measure, EEG and ET features in each interval. The second analysis was performed to find correlation of cognitive workload and EEG and ET features. The results proved that the best cognitive workload measures are selected eye movement and pupil dilation measures.

Comparison of the ICA and PCA methods in correction of EEG signal artefacts

The paper presents application and comparison of two methods based on the blind source separation problem: Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as well as combining these methods. Both methods might be applied in the task of eliminating artefacts from the electroencefalography (EEG) signal. Such artefacts might cover eye-blinks, muscle artefacts etc. The case study described in the paper presents the results of correcting various kinds of artefacts using these methods and its comparison to manual artefact detection performed by an expert.

The Artifact Subspace Reconstruction (ASR) for EEG Signal Correction. A Comparative Study

The paper presents the results of a comparative study of the artifact subspace re-construction (ASR) method and two other popular methods dedicated to correct EEG artifacts: independent component analysis (ICA) and principal component analysis (PCA). The comparison is based on automatic rejection of EEG signal epochs performed on a dataset of motor imagery data. ANOVA results show a significantly better level of artifact correction for the ASR method. What is more, the ASR method does not cause serious signal loss compared to other methods.

Measuring Cognitive Workload in Arithmetic Tasks Based on Response Time and EEG Features

The aim of the present paper is to verify whether the cognitive load can be evaluated through the analysis of the examined person’s response time and extracted EEG signal features. The research was based on an experiment consisting of six intervals ensuring various cognitive load level of arithmetic tasks. The paper describes in details the analysis process including signal pre-processing with artifact correction, feature extraction and outlier detection. Statistical verification of EEG band differences, response time and error rate in intervals was realised. Statistical correlations were found between EEG features and response time, however, the correlation strength increased inside the groups of intervals of similar cognitive workload level. Evoked related potentials were also analysed and their results confirmed the statistical outcomes.