  Real-time EEG-based Emotion Recognition (EEG-ER) with consumer-grade EEG
devices involves classification of emotions using a reduced number of channels.
These devices typically provide only four or five channels, unlike the high
number of channels (32 or more) typically used in most current state-of-the-art
research. In this work we propose to use Discrete Wavelet Transforms (DWT) to
extract time-frequency domain features, and we use time-windows of a few
seconds to perform EEG-ER classification. This technique can be used in
real-time, as opposed to post-hoc on the full session data. We also apply
baseline removal preprocessing, developed in prior research, to our proposed
DWT Entropy and Energy features, which improves classification accuracy
significantly. We consider two different classifier architectures, a 3D
Convolutional Neural Network (3D CNN) and a Support Vector Machine (SVM). We
evaluate both models on subject-independent and subject dependent setups to
classify the Valence and Arousal dimensions of an individual's emotional state.
We test them on both the full 32-channel data provided by the DEAP dataset, and
also a reduced 5-channel extract of the same dataset. The SVM model performs
best on all the presented scenarios, achieving an accuracy of 95.32% on Valence
and 95.68% on Arousal for the full 32-channel subject-dependent case, beating
prior real-time EEG-ER subject-dependent benchmarks. On the subject-independent
case an accuracy of 80.70% on Valence and 81.41% on Arousal was also obtained.
Reducing the input data to 5 channels only degrades the accuracy by an average
of 3.54% across all scenarios, making this model appropriate for use with more
accessible low-end EEG devices.
