Abstract
The perception of an external stimulus is not just stimulus-dependent but is also influenced by the ongoing brain activity prior to the presentation of stimulus. In this work, we directly tested whether spontaneous electroencephalogram (EEG) signal in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis using machine learning framework. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Using features based on the Regularized Common Spatial Patterns (RCSP) in a machine learning classifier, we demonstrated that prestimulus brain activities could discriminate face and no-face perception with an accuracy of 73.15%. The channels corresponding to the maximal coefficients of spatial pattern vectors may be the channels most correlated with the task-specific sources, i.e., frontal and parieto-occipital regions activate for ‘face’ and ‘no-face’ imagery class, respectively. These findings suggest a mechanism of how prior expectations in the prestimulus period may affect post-stimulus decision-making.
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Barik, K., Jones, R., Bhattacharya, J., Saha, G. (2019). Investigating the Influence of Prior Expectation in Face Pareidolia using Spatial Pattern. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_38
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