Key result
EEG-based machine learning models, particularly k-nearest neighbor, predicted premature video skipping with 82.2% accuracy and viewer interest with 78.3% accuracy.
Why the study?
Monitoring mental state in response to commercials is of interest in neuromarketing, but methods to predict premature video skipping from EEG recordings were lacking.
EEG recordings combined with machine learning can predict viewer interest and video skipping behavior with over 80% accuracy, offering potential applications in neuromarketing.
Brain–computer interfacing has enjoyed growing attention, not only due to the stunning demonstrations with severely disabled patients, but also the advent of economically viable solutions in areas such as neuromarketing, mental state monitoring, and future human–machine interaction. An interesting case, at least for neuromarketers, is to monitor the customer’s mental state in response to watching a commercial. In this paper, as a novelty, we propose a method to predict from electroencephalography (EEG) recordings whether individuals decide to skip watching a video trailer. Based on multiscale sample entropy and signal power, indices were computed that gauge the viewer’s engagement and emotional affect. We then trained a support vector machine (SVM), a k-nearest neighbor (kNN), and a random forest (RF) classifier to predict whether the viewer declares interest in watching the video and whether he/she decides to skip it prematurely. Our model achieved an average single-subject classification accuracy of 75.803% for skipping and 73.3% for viewer interest for the SVM, 82.223% for skipping and 78.333% for viewer interest for the kNN, and 80.003% for skipping and 75.555% for interest for the RF. We conclude that EEG can provide indications of viewer interest and skipping behavior and provide directions for future research.
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Libert et al. (2019) studied this question. EEG-based prediction models (SVM, kNN, RF) was evaluated on Prediction of premature video skipping and viewer interest. EEG-based machine learning models, particularly k-nearest neighbor, predicted premature video skipping with 82.2% accuracy and viewer interest with 78.3% accuracy.
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