Unveiling Mental Imagery: Enhanced Mental Images Reconstruction using EEG and the Bubbles Method

The exact nature of the visual features that are brought to consciousness when one is engaging in mental imagery is still difficult to study empirically. The few studies that have attempted to reconstruct mental images obtained poor quality results due to a poor sampling of the “scene space”. The aim of our study was to reconstruct better quality mental images using electroencephalography (EEG) and the Bubbles method, a technique that randomly samples visual information in an image. We hypothesize that the reconstructed mental images would reveal key visual features of the images and that verbal instructions (e.g., imagine the man and not the car in the image) could modulate the reconstructed image. We recorded the brain activity of participants (preliminary sample: N = 7, 4 males, mean age = 22.4) during two alternating tasks divided into 6 two-hour sessions. In the perception task, participants were presented with two images through different sets of randomly located Gaussian apertures or “bubble masks” (1,500 trials per image). In the mental imagery task, participants were shown the two stimuli successively and asked to imagine the first or the second one, in its entirety or in part (450 trials per image, including ⅓ object-specific trials). For each participant and for each image, we correlated the EEG activity patterns between the mental imagery and visual perception tasks. The bubbles masks, weighted by corresponding correlation coefficients, were then summed to generate “classification images'' of mental images. Comparing these classification images between the object-specific imagery trials, we found that the content of mental images could, indeed, be modulated by instructions for some participants. This study not only contributes to the understanding of the neural mechanisms underlying imagery, but also offers a promising avenue for optimizing the communication methods through brain-computer interfaces.