Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing
Yoelvis Moreno-Alcayde,
V. Javier Traver,
Luis A. Leiva
Abstract:Brain-Computer Interfacing (BCI) has shown promise in Machine Learning (ML) for emotion recognition. Unfortunately, how data are partitioned in training/test splits is often overlooked, which makes it difficult to attribute research findings to actual modeling improvements or to partitioning issues. We introduce the “data transfer rate” construct (i.e., how much data of the test samples are seen during training) and use it to examine data partitioning effects under several conditions. As a use case, we conside… Show more
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