Analysis of the recorded EEG data from the experimental group

In Figure 11.2 r2-values for each frequency bin and channel are shown as heatmap for each subject of the experimental group. As postulated in literature, a strong difficulty related effect in the alpha-frequency over the parieto-occipital electrodes (channels 10-16) can be detected in 8 out of 13 subjects. The effect is also prominent in the delta- and theta-frequency band for 9 out of 13 subjects, whereas rather the frontal-central electrodes are affected. Diverse patterns are shown in the lower and higher beta-frequency band, which might be caused due to muscle artifacts. For 8 out of 13 subjects, channel 16 (i.e., electrode POz) has an independent pattern compared to adjacent electrodes. This leads to the suggestion that the electrode was broken and interfering signals were measured during this recording.

To ensure the broken electrode POz does not influence the workload prediction erro- neously, the weights of each frequency bin at each electrode are plotted in Figure 11.3. The weights reflect the importance of a feature for the workload prediction. The higher the weight of a feature, the more it has an influence on the online workload prediction.

11.5 Neurophysiological features

Figure 11.2:Heatmap for each subject of the experimental group, presenting r2-values between the power at each frequency bin for each electrode and the information content Q. High squared correlations are indicated by red, while low squared correlations are indicated by blue. Participants shown here are enumerated from top left to bottom right line by line.

Figure 11.3:Heatmap for each subject of the experimental group, presenting the regression weight of each frequency bin at each electrode. Red indicates features which are strongly weighted and thus important for the regression model, whereas features plotted in blue have lower weights and are thus not important for the online workload prediction. Participants shown here are enumerated from top left to bottom right line by line.

11 Online workload detection in an adaptive learning environment

Figure 11.4:Heatmaps averaged over all subjects of the experimental group. Left: r2-values be- tween the power at each frequency bin for each electrode and the information content Q. High squared correlations are indicated by red, while low squared correlations are indicated by blue. Right: The regression weight of each frequency bin at each electrode. Red indicates features which are strongly weighted and thus important for the regression model, whereas features plotted in blue have lower weights and are thus not important for the online workload prediction.

For the major part of the subjects, the features in the theta- and alpha-frequency band are strongly weighted, regardless of channel 16. Merely in subject 4, 5, 6, 10 and 11, a strong weighting of features from channel 16 in the lower and upper beta-frequency bands can be detected, which might influence the online workload prediction negatively. Averaged over all subjects (see Figure 11.4) especially features from the delta- and theta-frequency bands across the central, parietal and occipital electrodes are strongly weighted, whereas only a few features from the beta-frequency band are used for the online workload prediction. In Figure 11.2 the alpha- and theta-frequency bands show an effect related to task diffi- culty and thus to workload. These strong effects cannot be observed in the heatmap (see Figure 11.4 left) and topography plot (see Figure 11.5), where the average power spec-

Figure 11.5:Topography plot averaged over the power spectra of all subjects of the experimental group, presenting r2-values between the power at each frequency bin for each electrode and the in- formation content Q. High squared correlations are indicated by red, while low squared correlations are indicated by blue.

11.6 Behavioral results

Figure 11.6:Topography plot averaged over the power spectra of all subjects in the control group, presenting r2-values between the power at each frequency bin for each electrode and the information content Q. High squared correlation is indicated by red, whereas a low squared correlation is presented in blue.

trum over all subjects is shown. In Figure 11.5, a clear difficulty related effect over the central electrodes can be recognized (r2 = 0.1) in the delta-frequency band, while there is no effect detectable in the theta-frequency band. A small effect (r2 = 0.04) can be mea- sured over the parieto-occitiptal electrodes in the alpha-frequency band. As in the heatmap (Figure 11.2), strong diverse patterns are shown in the lower and less prominent in the up- per beta-frequency band (r2 = 0.16). This might be caused due to muscle artifacts. The increased r2-value in the theta- and alpha-frequencies, while averaging the power spectra over all subjects, can be explained by varying frequency band boundaries between subjects. The patterns in the delta-frequency band seem to be consistent over the cross-subject stud- ies (see chapter 10) and therefore a robust objective measurement to predict the amount of workload during addition tasks.

11.5.2 Analysis of the recorded EEG data from the control group

The cognitive load theory points out, that in a learning phase, levels of workload should be held in an optimal range for successful learning. Therefore, the recorded EEG data of the control group was analyzed offline, to be able to estimate the amount of workload for each trial and subject subsequently.

The calculated r2-values averaged over the power spectrum of all subjects of the control group are shown as topography plots in Figure 11.6. A strong difficulty related effect over the parieto-occipital and frontal-central electrodes can be recognized in the delta (r2 = 0.02), as well as in the lower and upper beta-frequency band (r2 = 0.03). The effects measured in the theta- and alpha-frequency band might be caused due to eye-movements and muscle artifacts.

11.6 Behavioral results

The following sections report on the behavioral results of the experimental and control group, while using the two diverse learning environments.

11 Online workload detection in an adaptive learning environment