A Mutual Information Based Adaptive Windowing of Informative EEG for Emotion Recognition

Piho, Laura; Tjahjadi, Tardi

doi:10.1109/taffc.2018.2840973

Cited by 84 publications

(34 citation statements)

References 53 publications

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“…This, in turn, indicates that our proposed method plays a positive role in emotion recognition applications. In another research [15], the highest percentage of accuracy among the studies conducted on emotion recognition was achieved. In other words, accuracy levels of 89.61% and 89.84% were reached in a two-level classification for the valence and arousal dimensions, respectively, while accuracy levels of 75.02% and 75.70% were achieved in a three-level classification for the arousal and valence dimensions, respectively.…”

Section: Comparison With Previous Studiesmentioning

confidence: 93%

“…In [14], a new technique is defined for classification of emotions in 4 binary classes in 2D arousal-valence space. In [15], the authors used 2400 different features for emotion recognition in both arousal and valence dimensions. They used many feature extraction techniques, and finally with 30 to 40 features they reported 89.84% and 89.61% accuracies in bilevel and 75.02% and 75.70% accuracies in multilevel arousal and valence classifications, respectively.…”

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confidence: 99%

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Application of multiscale fuzzy entropy features for multilevel subject-dependentemotion recognition

Lotfalinezhad¹,

Maleki²

2019

Turk J Elec Eng & Comp Sci

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Emotion recognition can be used in clinical and nonclinical situations. Despite previous works which mostly used time and frequency features of electroencephalogram (EEG) signals in subject-dependent emotion recognition issues, we used multiscale fuzzy entropy as a nonlinear dynamic feature. The EEG signals of the well-known Database for Emotion Analysis Using Physiological signals dataset was used for classification of two and three levels of emotions in arousal and valence space. The compound feature selection with a cost of average accuracy of support vector machine classifier was used to reduce feature dimensions. For subject-dependent systems, the proposed method is superior in comparison to previous works with 90.81% and 90.53% accuracies in two-level classification and 79.83% and 77.80% accuracies in three-level classification in arousal and valence dimensions, respectively.

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Section: Comparison With Previous Studiesmentioning

confidence: 93%

mentioning

confidence: 99%

Application of multiscale fuzzy entropy features for multilevel subject-dependentemotion recognition

Lotfalinezhad¹,

Maleki²

2019

Turk J Elec Eng & Comp Sci

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“…Portioning training data and test data form the same subject and adapting the classifier on a specific subject makes the task subject dependent which has improved results. For instance, in [24] classification of statistical features extracted from DEAP dataset achieves 82.76% and 82.77% for 2 valence and arousal levels, respectively with k-Nearest Neighbor (k-NN). But, here the lack of generalization is the cost to pay.…”

Section: Related Work On Deap Datasetmentioning

confidence: 99%

Unsupervised Learning in Reservoir Computing for EEG-Based Emotion Recognition

Fourati

Ammar

Medina

et al. 2022

IEEE Trans. Affective Comput.

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In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with a great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. Actually, the neural plasticity rules are a kind of unsupervised learning adapted for the reservoir, i.e. the hidden layer of ESN. More specifically, an investigation of Oja's rule, BCM rule and gaussian intrinsic plasticity rule was carried out in the context of EEG-based emotion recognition. The study, also, includes a comparison of the offline and online training of the ESN. When testing on the well-known affective benchmark "DEAP dataset" which contains EEG signals from 32 subjects, we find that pretraining ESN with gaussian intrinsic plasticity enhanced the classification accuracy and outperformed the results achieved with an ESN pretrained with synaptic plasticity. Four classification problems were conducted in which the system complexity is increased and the discrimination is more challenging, i.e. inter-subject emotion discrimination. Our proposed method achieves higher performance over the state of the art methods.

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“…Since the DEAP dataset is publicly available, it allows us to have a better perspective on the differences between the methods and on the variability in their performances. Piho and Tjahjadi [18] investigated reduced EEG data of emotions using mutual information-This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”

Section: Related Workmentioning

confidence: 99%

EEG Classification by Factoring in Sensor Spatial Configuration

et al. 2021

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Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders and neurological abnormalities. Enhanced analysis and classification of EEG signals can help improve performance in classifying the disorders and abnormalities. A new approach is examined here for enhancing EEG classification performance using a novel model of data representation that leverages knowledge of spatial layout of EEG sensors. An investigation of the performance of the proposed data representation model provides evidence of consistently higher classification accuracy of the proposed model compared with a model that ignores the sensor layout. The performance is assessed for models that represent the information content of the EEG signals in two different ways: a one-dimensional concatenation of the channels of the frequency bands and a proposed image-like two-dimensional representation of the EEG channel locations. The models are used in conjunction with different machine learning techniques. Performance of these models is examined on two tasks: social anxiety disorder classification, and emotion recognition using a dataset, DEAP, for emotion analysis using physiological signals. We hypothesize that the proposed two-dimensional model will significantly outperform the one-dimensional model and this is validated in our results as this model consistently yields 5-8% higher accuracy in all machine learning algorithms investigated. Among the algorithms investigated, Convolutional Neural Networks provide the best performance, far exceeding that of Support Vector Machine and k-Nearest Neighbors algorithms.

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A Mutual Information Based Adaptive Windowing of Informative EEG for Emotion Recognition

Cited by 84 publications

References 53 publications

Application of multiscale fuzzy entropy features for multilevel subject-dependentemotion recognition

Application of multiscale fuzzy entropy features for multilevel subject-dependentemotion recognition

Unsupervised Learning in Reservoir Computing for EEG-Based Emotion Recognition

EEG Classification by Factoring in Sensor Spatial Configuration

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