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Brain Informatics
Fig. 10 | Brain Informatics

Fig. 10

From: Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Fig. 10

Biofeedback for VRET: to reduce the interference, we had to target to minimise the use of the number of sensors. We planned to use heart rate, so it was challenging to calculate heart rate using emotive EPOC flex. We used electrodes FT9 and FT10 to determine our heart rate. We placed the probe across the neck. We first performed the baseline correction for the acquired raw signal and then filtered the data. Afterwards, we calculated the bipolar difference to determine the heart rate. On the other side, we used 5 s window for our EEG data acquisition. Then we systematically did the baseline correction, filtered the data and used electrodes F3, F4, AF3 and AF4 to calculate the literality index. Then we used calculated heart rate and literality index as forms of biofeedback

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