ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2022
Applying Big Transfer-based classifiers to the DEAP dataset.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of the most popular datasets for emotional classification. In this study we generated heat maps from spectral data within the neurological signals found in the DEAP dataset. To account for the class imbalance within this dataset, we then discarded images belonging to the larger class. We used these images to fine-tune several Big Transfer neural networks for binary classification of arousal, valence, and dominance affective states. Our best classifier was able to achieve greater than 98% accuracy and 990% balanced accuracy in all three classification tasks. We also investigated the effects of this balancing method on our classifiers.
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