Commercial Content Evaluation from Physiological Signal Analysis


The main goal of this project is to develop an automatic system for commercial content classification. This classification is performed according to the quality of the ad and taking into account physiological information. In addition, some conclusions about the cognitive cerebral processes and the human behaviour during content exposure are extracted.


From electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR) and respiration signal is possible to extract relevant features to quantify attention, memorization and pleasantness levels. The main objective of this project is to develop a system able to classify audio-visual contents using feature extraction from the physiological registers. These registers are acquired while participants are watching a documentary in which different interleaved commercials appears.

After the signal pre-processing (performed to obtain reliable results) different metrics related to the temporal and frequency domains are computed to quantify the user’s feelings during the visualization of each commercial. Using this information automatic predictive models are learned using advanced Machine Learning algorithms.

Another objective of this project is to provide answer to different experimental questions about the human behaviours when commercial contents are exposed to the participants:

  • Are there differences in cerebral activity during the observation of commercials between the population that remember and forget the ad?
  • May the cerebral activity be objectively quantified and may be used to identify potential successful advertisements?
  • Is possible to automatically determine the video frames that produce a significant increase or decrease in the cerebral activity and correlate this cerebral activity with other physiological measures?


Internal financing


2013 to 20150


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