1 Affective Learning with an EEG Approach Xiaowei Li School of Information Science and Engineering, Lanzhou University, Lanzhou, China

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 2Relevant 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.

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

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

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.

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 Comparison of Frequency Spectrum Frequency/Hz Power Spectrum Density N=128 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 ECEO 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!