Outline

1. Affective Learning with an EEG ApproachXiaowei LiSchool of Information Science and Engineering, Lanzhou University, Lanzhou, China lixwei@lzu.edu.cn

2. Outline • Introduction of Affective Learning; • Relevant Research in Affective Learning; • Challenges; • Affective Learning Study with an EEG Approach; • Conclusion.

3. 1. Introduction of Affective Learning • Computer’s role in learning; • Affective Learning.

4. 1.1 Computer’s Role in Learning State of the Art: • Learning facilities; • Extension of physical learning; • Knowledge management and transfer; • Ubiquitous accessibility.

5. 1.2 Affective Learning Positive affect may: 1、Trigger innovative thinking in learning process; 2、Enhance creativity and flexibility in learning; 3、Approach the expected outcomes in learning.

6. 1.2 Affective Learning (Cont.) • Affective learning activities are directed at coping with feelings that arise during learning, and that positively or negatively impact the learning process. --- J. Vermunt,1996 • Affective Learning research is intended to analyze human affect fluctuation and its influence in learning.

7. Relevant Research in Affective Learning • MIT; • Delft University of The Netherlands.

8. Affective Learning Research in MIT • A system was designed at the Media Lab for automated recognition of a child’s interest level in natural learning situations; • Using a combination of information from chair pressure patterns sensed using Tekscan pressure arrays and from upper facial features sensed using an IBM BlueEyes video camera.

10. Evaluation • Achieved an accuracy of 76% on affect category recognition from chair pressure patterns, and 88% on nine ‘basic’ postures that were identified as making up the affective behaviours .

11. Emotion Recognition Research in Delft University Experimental setup (left) and Self Assessment Manikin (right).

12. Emotion Recognition Research in Delft University • Participants’ EEG signals were recorded and processed when they were viewing pictures selected from International Affective Picture System (IAPS) database; • Participant was asked to rate his/her emotion on a Self-Assessment Manikin ; • Evaluate the emotions reflected from EEG signals if matching with Self-Assessment Manikin .

13. Conclusion • These results show that EEG data contains enough information to recognize emotion.

14. 3 Challenges • Designing affective learning experiments on learners while introducing limited disturbances on learners is one the challenges; • Most existing studies obtain affective information through speech, motion, gesture, facial expression, etc; • New techniques need to be introduced to rich the ways of deeper understanding learners’ affect such as EEG approach.

15. 4 Affective Learning with an EEG Approach • EEG signals ; • Affective learning study based on EEG; • Prototyping.

16. 4.1 EEG Signals • Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain.

17. Part of EEG Wave Groups •Beta (14 – 26 Hz) waking rhythm associated with active thinking; • Alpha (8 – 13 Hz) indicate a relaxed awareness and inattention; • Theta (4 – 7 Hz) appears as consciousness slips into drowsiness; • Delta (0.5 – 4 Hz) associated with deep sleep.

18. A Sample of EEG Signals A sample of EEG signal collected by the Nexus, and shown by BioTrace+ Software

19. Data Exported From RAW Waves

20. EEG Electrode Placement and Equipment 32-channels 128-channels Nexus-16

21. 4.2 Affective learning study based on EEG • Evaluation of existing e-learning website through analysis of learners’ EEG data during the learning process; • Development of intelligent e-learning website which can feedback appropriate content and alert to learners during learning process through real-time analysis algorithm on EEG data.

22. 4.3 Prototyping

23. Affective Learning Prototyping

24. Two Methods for EEG Processing • Amplitude Analysis • Frequency Analysis

25. Amplitude Analysis Alpha amplitude shown in BioTrace+ Software

26. When users concentrate on some learning content 1. Amplitude of alpha waves tends to decrease; 2. Amplitude of theta increases.

27. Amplitude Comparison Between Two Alpha Waves While a Learner in Different Moods Series 1 implies that the learner focuses on content; Series 2 implies that the learner is losing attention.

28. Frequency Analysis • Power of alpha wave can be obtained via integral on frequency domain, which is much more simple than that on time domain; • The power of alpha wave, which is extracted while user is concentrated, is lower than that of absent-minded .

29. 100 N=128 90 80 70 60 Power Spectrum Density 50 40 30 20 10 0 6 7 8 9 10 11 12 13 Frequency/Hz Comparison of Frequency Spectrum Series 1 implies that the learner focuses on content; Series 2 implies that the learner is losing attention.

30. Comparison of Power of Alpha Wave EC EO EC: eyes closed EO: eyes open

31. 5 Conclusions • The affective learning study based on EEG signals requires a new insight; • New techniques, especially EEG based feedback techniques may deepen understanding learners’ affect to enhance their learning outcomes; • Combination or development of innovative techniques and theories is the key in bio-signals based, e.g. EEG, affective learning research.

32. Thanks!