Journal ArticleDOI
Automated emotion recognition based on higher order statistics and deep learning algorithm
TLDR
An automated classification of emotions-labeled EEG signals using nonlinear higher order statistics and deep learning algorithm has the potential for accurate and rapid recognition of human emotions.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2020-04-01. It has received 107 citations till now. The article focuses on the topics: Higher-order statistics & Discrete wavelet transform.read more
Citations
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Journal ArticleDOI
Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network.
TL;DR: The proposed MLF-CapsNet is an end-to-end framework, which can simultaneously extract features from the raw EEG signals and determine the emotional states and incorporates multi-level feature maps learned by different layers in forming the primary capsules so that the capability of feature representation can be enhanced.
Journal ArticleDOI
A new fractal pattern feature generation function based emotion recognition method using EEG
TL;DR: An automated EEG based emotion recognition method with a novel fractal pattern feature extraction approach is presented and has been tested on emotional EEG signals with 14 channels using linear discriminant, k-nearest neighborhood, support vector machine, and SVM.
Journal ArticleDOI
Seizures classification based on higher order statistics and deep neural network
TL;DR: An automated seizures classification technique using the nonlinear higher-order statistics and deep neural network algorithms and achieves reliable classification accuracy for both categories, i.e., binary classes and three-classes of electroencephalogram (EEG) signals with the softmax classifier.
Journal ArticleDOI
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review
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.
Journal ArticleDOI
EMG hand gesture classification using handcrafted and deep features
TL;DR: The proposed approach combines handcrafted features from a time-spectral analysis, like mean absolute value (MAV), slope sign changes, peak frequencies, wavelet transform (WT) coefficients, etc, and deep features to create the feature vector, which is then classified using a multi-layer perceptron classifier (MLPC).
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI
Learning long-term dependencies with gradient descent is difficult
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.
Journal Article
The ten-twenty electrode system of the international federation
TL;DR: During the First International EEG Congress, London in 1947, it was recommended that Dr. Herbert H. Jasper study methods to standardize the placement of electrodes used in EEG (Jasper 1958).
Journal ArticleDOI
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Sander Koelstra,Christian Mühl,Mohammad Soleymani,Jong-Seok Lee,Ashkan Yazdani,Touradj Ebrahimi,Thierry Pun,Anton Nijholt,Ioannis Patras +8 more
TL;DR: A multimodal data set for the analysis of human affective states was presented and a novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool.
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