Meth o dology

The experiments were conducted in two laboratories with a free 3.5 *3.5 m space to move. However, the VR scene size was 5.3*7.0 m; thus, the participant could walk partially through VR scenes.

The VR scene was designed to let the participant walk in the laboratory size. The VR ground level was adjusted to the actual laboratory ground level by placing one of the controllers on the laboratory floor to calibrate the VR scene’s height and ground level. That created a real height of the VR scene and let the participant feel that he is living real environment in terms of dimensions and space size.

VR Headset

Mobile EEG Combined EEG_VR

Quest VR headset was adjusted combined together with the EEG Emotiv above the participant’s head. Figure 4 shows the combined EEGVR headsets system.

Experimental Procedure

The experiment started with letting the participant sit while wearing the EEG and the VR headsets. Then, the participant started navigating and walking through the first virtual scene and the EEG recording was started accordingly. The experimenter controlled the start and the end of the real-time EEG recording process. Real-time EEG were being recorded through Emotiv PRO for the 14 channels. The participant navigated each scene for 2 minutes. Then, the EEG recording was stopped first, and then the participant stopped navigating the virtual scene. Then, the participant sat quietly again for 3 minutes while wearing the EPOC and the VR headsets without recording EEG, to measure the baseline for the next scene/recording.

Analysis & Results

The raw EEG data was gathered and calculated into alpha (α, 8-13Hz) and beta (β, 14-30 Hz) bands. Once the baseline recording was finished, the EEG data recordings started accordingly. The raw signals were pre-processed using high-pass filter and then Fast Fourier transform (FFT) was applied to obtain band powers in µV2. Alpha (8 – 13 Hz), low beta (14-20 Hz) and high beta (20-30 Hz) were averaged across all electrodes as they are strong and widespread activity.

Furthermore, a machine learning algorithms to predict relaxation degree of an architectural space with values inputs of alpha, low beta and high beta.

The study investigated through subjective and objective physiological tools that warm colours can increase our attention but also can decrease our relaxation. However, cold colours positively impact our relaxation in the space. In contrast, complex patterns can reduce our relaxation in space.

Result Sample: Average alpha activity across the two pairs of rooms.