EEG-based emotion recognition using simple recurrent units network and ensemble learning

TL;DR: In this article , a review of the EEG-based emotion recognition methods is presented, including feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods.

TL;DR: The relevantly scientific literature in the past five years is investigated and the emotional feature extraction methods and the classification methods using EEG signals are reviewed, finding that emotion recognition rapidly becomes a multiple discipline research field through EEG signals.

TL;DR: A comprehensive and systematic review of emotion recognition techniques of the current decade and an introduction to various emotion models, stimuli used for emotion elicitation, and the background of existing automated emotion recognition systems are provided.

TL;DR: According to the high accuracy of the proposed method, it can be used to develop the human-computer interface system and compared with other hand-crafted methods based on MLP and DBM classifiers and achieved promising results in comparison with similar approaches.

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.

TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.

TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.

TL;DR: This work shows why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases, and exposes a trade-off between efficient learning by gradient descent and latching on information for long periods.