  Emotions play a crucial role in human interaction, health care and security
investigations and monitoring. Automatic emotion recognition (AER) using
electroencephalogram (EEG) signals is an effective method for decoding the real
emotions, which are independent of body gestures, but it is a challenging
problem. Several automatic emotion recognition systems have been proposed,
which are based on traditional hand-engineered approaches and their
performances are very poor. Motivated by the outstanding performance of deep
learning (DL) in many recognition tasks, we introduce an AER system (Deep-AER)
based on EEG brain signals using DL. A DL model involves a large number of
learnable parameters, and its training needs a large dataset of EEG signals,
which is difficult to acquire for AER problem. To overcome this problem, we
proposed a lightweight pyramidal one-dimensional convolutional neural network
(LP-1D-CNN) model, which involves a small number of learnable parameters. Using
LP-1D-CNN, we build a two level ensemble model. In the first level of the
ensemble, each channel is scanned incrementally by LP-1D-CNN to generate
predictions, which are fused using majority vote. The second level of the
ensemble combines the predictions of all channels of an EEG signal using
majority vote for detecting the emotion state. We validated the effectiveness
and robustness of Deep-AER using DEAP, a benchmark dataset for emotion
recognition research. The results indicate that FRONT plays dominant role in
AER and over this region, Deep-AER achieved the accuracies of 98.43% and 97.65%
for two AER problems, i.e., high valence vs low valence (HV vs LV) and high
arousal vs low arousal (HA vs LA), respectively. The comparison reveals that
Deep-AER outperforms the state-of-the-art systems with large margin. The
Deep-AER system will be helpful in monitoring for health care and security
investigations.
