Emotion Recognition with Machine Learning Using EEG Signals

Bazgir, Omid; Mohammadi, Zohreh; Habibi, Seyed Amir Hassan

doi:10.1109/icbme.2018.8703559

Cited by 100 publications

(42 citation statements)

References 16 publications

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“…Parameter setup: The considered machine learning approaches have various parameters that need to be configured. The parameters setup for offline classifiers (SVM, MLP and DT) were taken directly from emotion classification research papers [ 47 , 48 , 49 ]. The parameter setups are the following:…”

Section: Methodsmentioning

confidence: 99%

“…SVM: For SVM, the C (regularization parameter) is set to 1.0 and radial basis kernel (rbf) is used as specified in [ 49 ].…”

Section: Methodsmentioning

confidence: 99%

“…• SVM: For SVM, the C (regularization parameter) is set to 1.0 and radial basis kernel (rbf) is used as specified in [49]. [50] on one subject using RECS, where we have varied the learning rate for SGD from 0 to 1 with a step size increment of 0.01.…”

mentioning

confidence: 99%

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Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Nandi

Xhafa

Subirats

et al. 2021

Sensors

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In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners’ emotions are crucial for e-learning system because they promote or restrain the learning. Many researchers have investigated the impacts of emotions in enhancing and maximizing e-learning outcomes. Several machine learning and deep learning approaches have also been proposed to achieve this goal. All such approaches are suitable for an offline mode, where the data for emotion classification are stored and can be accessed infinitely. However, these offline mode approaches are inappropriate for real-time emotion classification when the data are coming in a continuous stream and data can be seen to the model at once only. We also need real-time responses according to the emotional state. For this, we propose a real-time emotion classification system (RECS)-based Logistic Regression (LR) trained in an online fashion using the Stochastic Gradient Descent (SGD) algorithm. The proposed RECS is capable of classifying emotions in real-time by training the model in an online fashion using an EEG signal stream. To validate the performance of RECS, we have used the DEAP data set, which is the most widely used benchmark data set for emotion classification. The results show that the proposed approach can effectively classify emotions in real-time from the EEG data stream, which achieved a better accuracy and F1-score than other offline and online approaches. The developed real-time emotion classification system is analyzed in an e-learning context scenario.

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Section: Methodsmentioning

confidence: 99%

“…SVM: For SVM, the C (regularization parameter) is set to 1.0 and radial basis kernel (rbf) is used as specified in [ 49 ].…”

Section: Methodsmentioning

confidence: 99%

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Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Nandi

Xhafa

Subirats

et al. 2021

Sensors

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“…In our first approach, we modeled the taxa counts in a sample using a Multivariate Poisson Log-Normal (MPLN) (described in chapter 3) distribution [46,69]. The mixture model framework consists of K MPLN distributions with different underlying parameters.…”

Section: Several Methods Have Been Proposed For Constructing a Singlementioning

confidence: 99%

Algorithms for inferring multiple microbial networks

Tavakoli

Yooseph

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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The interactions among the constituent members of a microbial community play a major role in determining the overall behavior of the community and the abundance levels of its members. These interactions can be modeled using a network whose nodes represent microbial taxa and edges represent pairwise interactions. A microbial network is a weighted graph that is constructed from a sample-taxa count matrix, and can be used to model co-occurrences and/or interactions of the constituent members of a microbial community. The nodes in this graph represent microbial taxa and the edges represent pairwise associations amongst these taxa. A microbial network is typically constructed from a sample-taxa count matrix that is obtained by sequencing multiple biological samples and identifying taxa counts. From large-scale microbiome studies, it is evident that microbial community compositions and interactions are impacted by environmental and/or host factors. Thus, it is not unreasonable to expect that a sample-taxa matrix generated as part of a large study involving multiple environmental or clinical parameters can be associated with more than one microbial network. However, to our knowledge, microbial network inference methods proposed thus far assume that the sample-taxa matrix is associated with a single network. This dissertation addresses the scenario when the sample-taxa matrix is associated with K microbial networks and considers the computational problem of inferring K microbial networks from a given sample-taxa matrix. The contributions of this dissertation include 1) new frameworks to generate synthetic sample-taxa count data; 2)novel methods to combine mixture modeling with probabilistic graphical models to infer multiple interaction/association networks from microbial count data; 3) dealing with the compositionality aspect of microbial count data;4) extensive experiments on real and synthetic data; 5)new methods for model selection to infer correct value of K.

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“…Table 3 in [12] presents some of the most popular features extracted from time-series including temporal, statistical, spectral, linear, and/or non-linear features. Classifiers used to recognize emotional states, typically, regard supervised learning including k nearest neighbor (kNN) [14][15][16][17], support vector machine (SVM) [17][18][19], Naive-Bayes (NB) [17], quadratic discriminant analysis (QDA) [20], artificial neural networks [21,22]. Furthermore, unsupervised and semi-supervised learning algorithms are also used [12].…”

Section: Introductionmentioning

confidence: 99%

WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

et al. 2020

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Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

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Emotion Recognition with Machine Learning Using EEG Signals

Cited by 100 publications

References 16 publications

Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts

Algorithms for inferring multiple microbial networks

WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

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