Affective EEG Decoding Generalizes Across Colormap and Exposure Time

Viewing emotional pictures modulates electrocortical activity during the first second, with functional properties that reflect the type of processing that is being carried out. Recently, the investigation of electrocortical activity has been aided by machine learning techniques, such as multivariate pattern analysis (MVPA). Building on previous studies that used MVPA to classify between emotional and neutral stimuli, here we investigate electroencephalographic (EEG) changes while a sample of n = 15 participants viewed emotional and neutral scenes that could be presented in color or in grayscale, and for either a short (24 ms) or a long (6 s) exposure time. A linear classifier was used to classify EEG patterns as consequential to the viewing of emotional (pleasant, unpleasant) vs. neutral scenes, and to assess the extent to which scalp activation patterns are specific to the perceptual conditions under which a scene is viewed (i.e., color or greyscale, short vs. long exposure time) or generalize across viewing conditions. We observed that emotional content could be significantly decoded through MVPA, with earlier classification onset for pleasant-neutral vs. unpleasant-neutral classification. Moreover, this classification generalized across perceptual conditions, indicating that the symbolic meaning of natural scenes drives the emotional modulation of scalp activity. These results further indicate that, within the first second after the onset of natural scenes, emotional states can be decoded from the EEG signal, and that such learning can be applied to flexibly classify emotional states under perceptually different conditions.