EEG feature of fear downregulation

How do you identify fear immediately? How do you classify fear from EEG? How would you quantify fear? These questions bothered me in the beginning when I started researching the intersection of emotions, affective physiological responses and EEG signal processing. I imagined a biofeedback computer game / therapy program that helps people conquer their fears by rewarding them in the process. As time went on, my empirical knowledge and experimental data were extended, which, though changed the questions, not the vision. Here I'd like to outline how it all happened, what are the results and prospects of the research.

Experiment

While reading papers on EEG response upon fear stimulation, I was quite annoyed that the stimuli used in previous experiments were mostly IAPS images or video clips from horror movies (yeah, The Shining) completely taken out of context. To this day I believe that such stimuli are not sufficient to evoke strong enough emotional responses in a time period higher than a couple of seconds, and thus unable to provide insight into the big picture of fear regulation.

The experimental design hasn't changed much since my previous measurements, but gameplay and webcam videos were also recorded this time, in addition to EEG, heart rate (HR), and galvanic skin response (GSR) vital signals. First open-, then closed-eye measurements were taken for Individual Alpha Frequency estimation. Participants then played the "daylight" version of a computer FPS game as the baseline measurement. Only the "night" version contained fear inducing stimuli.

Acquiring fear intensity from EEG

My bachelor's thesis project is about the measurement of fear using EEG signals, similarly to my previous lab work. In this case though I made the participants to play a horror game instead just inducing fear by the means of audio stimuli. Also, I attempted to obtain the intensity of the emotion not just whether it is present or not - in addition to EEG signals, self-assessment, heart rate (HR) and skin conductance (GSR) data were gathered to verify the intensity. If you are interested in the whole thing in detail you can read it here (or at the end of this post). Team of the Synetiq neuromarketing research company supervised my thesis. They helped me a lot in putting together the measurement layout, the preprocessing of HR, EEG, and GSR signals and in the analysis of results.

Course of measurement

The experiment consisted of six main parts: 1) first, baseline self-assessment, 2) open-, and closed-eye baseline measurements, 3) baseline gameplay, 4) second self-assessment, 5) fear inducing gameplay, and 6) the last self-assessment. In total, the experiment took about 30 to 40 minutes with the time spent on the placement of the measurement tools excluded.

Parts of the 30-minute measurement as of time – width of the boxes are proportional to the length of the corresponding events.

EEG data analysis of audio induced fear

My previous post ended with some statistics now I proceed to the analysis of the retrieved signals. The results and the measurement itself is rather instructive than useful and practical, so if you are interested in a working solution I have to disappoint you. I'm planning a measurement concept that is going to be much simpler and going to look at the problem from a different perspective. Also as I dived more into the literature, I found that there was no need for the 3-minute calm part, there was rather need for the measurements immediately before and after the effects played.

Hypothesis to test - frontal alpha asymmetry

Pattern of asymmetrical frontal EEG activity is found to distinguish positive (happiness, joy) from negative (fear, anger) emotions by Hell and Nitschke [James A. Coan, 2004]. Although, this hypothesis turned out to be false (at least other more supported hypothesis came), and instead some showed that it would be the indicator of valence - greater right frontal activity (which means greater left alpha power) compared to the left frontal activity indicates approach or engage stimulus and greater left frontal activity infer tendency to withdrawal stimulus. This is called Davidson's approach/withdrawal model [Davidson, 1993]. Here I should point out that activity is inversely related to alpha power, which means lower power reflects more activity and vice versa.

Alpha power means the values of the signal converted to frequency domain at 7 to 12 Hz. Here is the alpha power spatial representation of a fearful (withdrawal stimulus) person playing a horror game - not part of the main measurements. Here the alpha asymmetry model actually worked.

Measured audio induced fear of 24 subjects using EEG signals

For my current thesis I had measurements of EEG activity from 24 mostly student subjects. The intention was to capture moments of fear or anxiousness which are induced only using audio effects. What turned out early that it's pretty hard to do. How can you arouse anxiousness to people that have different concepts of what's frightening and what's not? How can you point out the exact moment, when that feeling occurred? Are just audio inputs enough for someone to experience real danger?