Predicting Video features from EEG and Vice versa
This work addresses the problem of cross-modal prediction between brain activity and visual features for applications in neuroscience or assistive technologies, but it is incremental as it builds on existing deep learning methods.
The paper tackled predicting facial or lip video features from EEG signals and vice versa using deep learning, achieving the ability to generate broad characteristics of video frames from EEG inputs as a first step toward high-quality synthesis.
In this paper we explore predicting facial or lip video features from electroencephalography (EEG) features and predicting EEG features from recorded facial or lip video frames using deep learning models. The subjects were asked to read out loud English sentences shown to them on a computer screen and their simultaneous EEG signals and facial video frames were recorded. Our model was able to generate very broad characteristics of the facial or lip video frame from input EEG features. Our results demonstrate the first step towards synthesizing high quality facial or lip video from recorded EEG features. We demonstrate results for a data set consisting of seven subjects.