  Affective brain-computer interfaces are a relatively new area of research in
affective computing. Estimation of affective states can improve human-computer
interaction as well as improve the care of people with severe disabilities. To
assess the effectiveness of EEG recordings in recognizing affective state, here
we used data collected in our lab as well as the publicly available DEAP
database. We also reviewed the articles that used the DEAP database and found
that a significant number of articles did not consider the presence of the
class imbalance in the DEAP. Failing to consider class imbalance creates
misleading results. Further, ignoring class imbalance makes the comparing
results between studies impossible, since different datasets will have
different class imbalances. Class imbalance also shifts the chance level, hence
it is vital to consider class bias while determining if the results are above
chance. To properly account the effect of class imbalance, we suggest the use
of balanced accuracy as a performance metric and its posterior distribution for
computing credible intervals. For classification, we used features mentioned in
the literature and additionally theta beta-1 ratio. Results from DEAP and our
data suggest that the beta band power, theta band power, and theta beta-1 ratio
are better feature sets for classifying valence, arousal, and dominance,
respectively.
