To predict the amount of workload, the previously calculated power spectrum was used as feature. Furthermore, the data was z-score normalized along the channels, to correct for inter-subject variability in the subjects baseline EEG power. Meaning for each trial the mean of each frequency bin equals zero. Based on the normalized data of nine subjects,
10 Cross-subject workload prediction
a linear ridge regression model with a fixed regularization parameter of λ = 0.001 was trained. For further analysis the number of electrodes were reduced to 17 inner electrodes (FPz, AFz, F3, Fz, F4, FC3, FCz, FC4, C3, Cz, C4, CPz, P3, Pz, P4, Oz, POz), to lower the influence of possible artifacts, which are most prominent on the outer channels. The goal of the present study was to develop an efficient and generalized prediction method which is able to differentiate and predict levels of workload. Therefore, a within-subject regression as well as a cross-subject regression were comparatively applied.
10.3.1 Within-subject regression
In a within-subject regression, training and testing of a regression model were performed on EEG data recorded from the same task and subject. This method was applied for each participant individually. Good differentiability and prediction of workload levels within each subject are required to apply cross-subject regression. For within-subject regression, a 10-fold cross validation was accomplished to verify the separability of the independent dataset, in order to show that it is possible to indicate workload differences in the EEG data.
10.3.2 Cross-subject regression
As mentioned in previous studies, the collection of training data for classification and pre- diction methods in combination with a real-world learning environment is challenging. Therefore, a method called cross-subject regression was applied. For cross-subject re- gression, a leave-one-subject-out validation was conducted to verify the separability of the independent datasets, in order to predict different workload levels in the EEG data. There- fore, EEG data recorded from n − 1 subjects are used for calibrating a regression model. Subsequently, the workload prediction is evaluated based on the remaining EEG data from subject n. Again, the ultimate goal of this approach is to overcome the challenge of col- lecting training data for classifier training in combination with learning environments. In this study, the regression model was trained on data of nine subjects to predict the difficulty level on a single-trial basis for the one remaining subject, resulting in a predicted Q-value for each trial. This process was repeated ten times, so the data of each subject was used once for testing. For the cross-subject regression, two different models of training were tested. Either the regression model was trained on all trials, or only trials with a Q < 6 were used for the regression model training.
10.3.3 Evaluating the prediction performance
Since single-trial prediction is not necessary in a learning environment, the regression out- put was additionally smoothed to get a more robust prediction at the expense of increased delay of the system. To smooth the data, the moving average with a window length of six trials was calculated, which still guaranteed a response time < 1 min of the system. This delay is feasible for the detection of workload since it is not recommendable to adapt an
10.4 Behavioral results
online learning environment every 8.5 sec (i.e., single trial duration). As criterion for per- formance evaluation of the presented prediction method, the CC to observe the statistical relationship between the actual and the predicted Q-values, as well as RMSE to examine the difference between the actual and the predicted Q-values were used.
10.4 Behavioral results
To ensure that the participants solved the tasks conscientiously, as well as to have an addi- tional parameter for task difficulty, the error rate of each subject was logged. An analysis of the performance data shows that the Q-value is a suitable measure for task difficulty since it correlates well (r = 0.945, p < 0.0001) with the percentage of correctly solved trials (see Figure 10.2).
Figure 10.2:Percentage of correctly solved trials depending on the degrees of difficulty of each trial measured by Q. Each subject is shown by the thin colored lines and the average over all subjects is shown by the bold black line.
Although, there are inter-individual differences, trials with Q < 2 were solved correctly in nearly all cases, while trials with the difficulty of Q = 4 were solved correctly in about 30 % of the trials. None of the subjects were able to solve trials with a Q ≥ 6. On average, all subjects solved 64.25 % of all 240 assignments correct. The best subject solved 72.08 % correctly, whereas the worst subject reached an accuracy of 56.25 % (see Table 10.1). Relative to the presented difficulty levels, of which 25 % were unlikely to be solved, this are realistic performance results. This leads to the assumption, that subjects participated conscientiously and tried to solve the tasks correctly.
Table 10.1:Percentage of correctly solved trials averaged over all trials for each individual subject, as well as the mean performance averaged over all subjects.
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 mean
10 Cross-subject workload prediction
10.5 Neurophysiological features
Analyzing the EEG data shows that task difficulty and thereby the individual’s workload capacity is reflected in the power spectrum (see Figure 10.3). In the delta frequency band, a strong difficulty related effect over the occipital electrodes can be recognized, while the effect in the theta-band smaller. These measured effects might be caused due to artifacts. In the alpha-frequency, a difficulty related effect can be seen over the frontal electrodes, which might be caused by eye-movements. Diverse patterns are shown in the lower and higher beta-frequency band, which might be caused due to muscle artifacts.
Figure 10.3:Topographic display of r2-values averaged over all ten subjects, showing the influence of the Q-values for each electrode in different frequency bands, before EOG artifact correction.
Figure 10.4:Topographic display of r2-values averaged over all subjects, showing the influence of the Q-values for each electrode in different frequency bands, after EOG artifact correction. Since the workload prediction should not just work because of eye-movements or mus- cle artifacts during various levels of difficulty, but because of changes in brain signals, further pre-processing steps were additionally conducted. First, an EOG-based regression described by Schlögl et al. [150] was applied, to remove the influence of eye movements. After calculating the power spectrum, the z-score normalization was replaced by a base- line correction to correct for inter-subject variability in the subjects baseline EEG power. Therefore, the first 30 trials (easy trials, with Q < 1) were used for normalization. The mean standard deviation for each frequency bin at each electrode was calculated over the first 30 trials and the remaining 210 trials were scaled according to these means and stan- dard deviations. The 30 trials used for normalization were not used further in the prediction process, neither for training nor testing the model.
Analyzing the cleaned, pre-processed EEG data, it can be seen that task difficulty and thereby the individual’s workload capacity is still reflected in the power spectrum (see Fig- ure 10.4). Compared to Figure 10.3, the r2 patterns of the EOG artifact corrected data