beta-frequency band being important for workload detecting in the EEG data. A reason for this widespread findings could be individual variations in the brain activity, as well as di- verse strategies which are processed in distinct brain areas, to solve such complex learning tasks. If learning tasks are too complex, it is not controllable which strategies learners are using to handle the assignments. Diverse strategies lead to significant differences in active brain regions, as well as varying patterns in the frequency bands. Thus, the more complex the task, the more important is an individual analysis for each subject, with customized feature extraction, as well as individual classifier training.
5.7.2 Confounds in EEG data induced by complex material
The results show, it is indeed possible to classify whether learners study realistic instruc- tional materials or if they are reading comic-strips, based on single-subject EEG data. Although the results are practically and methodologically interesting, it cannot be ensured that the features used for classification are not confounded with perceptual-motor artifacts. The angle theorems and comic-strips were perceptually not identical, potentially leading to differences in semantic processing or eye-movements. Thus, even if there are no obvi- ous motor confounds of task difficulty in this study, there might be nevertheless perceptual confounds, which might be picked up by the SVM. Furthermore, it is uncertain whether workload classifiers trained on realistic learning tasks really represent a measure of work- load.
5.8 Conclusion
The classification of two mental states in EEG data during learning angle theorems (high workload) and reading comic-strips (low workload) using machine learning algorithms has been investigated. Although the results are promising it cannot be assumed, that work- load classifiers trained on realistic tasks really represent a measure of workload. Diverse strategies, perceptual-motor confounds and semantic processing can lead to significant dif- ferences in the EEG data across subjects, as well as across the two types of presented material. An individual feature selection and classifier training is advisable, which is not feasible in real-world settings. Furthermore, the presented stimuli have to be revised for the next studies, so that the induced EEG data is comparable across stimuli and subjects.
6 Feature selection for workload
classification
The first study showed that EEG signals can be used for classifying cognitive workload states during two complete diverse stimuli. Although the results are promising, it cannot be assumed, that workload classifiers trained on realistic tasks really represent a measure of workload. Therefore, a second study will be introduced in this chapter, where the de- tection of characteristics in EEG signals with high consistency over all subjects will be focused. The presented stimuli were revised, so that the induced EEG data is comparable across stimuli and subjects. The characteristics in the EEG signals should validly and re- liably represent cognitive workload states during solving math-tasks in varying difficulty levels. Furthermore, various signal pre-processing steps as well as diverse feature extrac- tion methods were implemented and calculated, to get the optimal preferences for further studies and analyses. The results presented in this chapter are exemplary for individual subjects and were partially presented and published in [132, 133].
6.1 Study design
In this section, a short introduction to the participants data, the task design, as well as the procedure will be given.
6.1.1 Participants and EEG recordings
In the experiment, seven subjects (4 female, 3 male) in the age of 22 – 28 years with a mean age of 25.3, participated voluntarily. A set of 32 active electrodes (actiCap BrainProducts GmbH) attached to the scalp, were used to record EEG signals. 29 electrodes were placed according to the extended International Electrode (10-20) Placement System. EOG was recorded through placement of the three remaining electrodes: two placed horizontally at the outer canthi of both eyes and one placed in the middle of the forehead between the eyes. EEG and EOG signals were amplified by two 16-channel biosignal amplifier systems (g.USB Generation 3.0, gTec). The sampling rate was 512 Hz and the impedance of each electrode was < 5 kΩ. EEG data was high-pass filtered at 0.1 Hz and low-pass filtered at 100 Hz during the recording. Furthermore, a notch-filter was disposed between 48 Hz − 52 Hz to filter the noise of the electrical circuit.
6 Feature selection for workload classification Pre- Test Post- Test difficult Assignment 45 Time (sec) Learning Session 10 Res 45 easy Assignment 10 Res
Figure 6.1:Schematic display of one EEG-trial during a learning session (Res = response).
6.1.2 Paradigm
The experiment has a within-subject design, which aimed at differentiating between high and low workload by means of EEG data. Participants were asked to learn angle theorems and to read information of varied complexity from diagrams. The experiment comprised three phases. In phase 1, the subjects had to solve a pre-test to survey their knowledge of angular geometry and diagrams prior to the study. Phase 2 consisted of four learning cycles, 17 min each. In cycle 1 and 3, the participants were asked to study four different angle theorems provided by Schwonke et al. [128]. Each angle theorem was alternatingly presented in an easy and a more complex way. The easy presentation supported the learning process of each learner with color-coding the angles and not using split attention effects. In the difficult presentation mode, the angles were black and white with no color-coding. Furthermore, a split attention effect was integrated. In cylce 2 and 4, the participants were asked to work with information of four different types of diagrams, in an easy and a more complex way (based on Schuh et al. [134]). In the easy condition, subjects only had to read out a number. During the difficult presentation mode they had to compare two relations. Each angle theorem and diagram was presented for 45 sec. Each cycle started with the more complex stimulus, followed by the same stimulus in an easier way. Subsequently, the next difficult stimulus appeared. The temporal sequence of one EEG trial is depicted in Figure 6.1. All in all, each participant studied 8 difficult and 8 easy angle theorems and worked with 8 complex as well as 8 effortless diagram information. Finally, a post-test had to be accomplished in phase 3. Participants had to solve the same assignments as in the pre-test, to achieve a direct comparison of their knowledge before and after the learning cycles. The whole experiment took approximately two hours for each subject.
6.2 Data pre-processing
For further steps, EEG data during the four learning and application cycles of each subject were concatenated. This resulted in two datasets. Session 1 and 3 were concatenated to one long “angle-theorem” session. Session 2 and 4 yield one long “diagram” session. The data was down sampled from 512 Hz to 300 Hz to speed up the data analysis. Linear drifts of the baseline were removed in the EEG signals, with the application of a detrend function. Furthermore, a Butterworth-Filter was used as high-pass filter at 0.5 Hz as well as low-pass filter at 48 Hz. Additionally a Common Average Reference Filter (CAR) was applied. CAR