EEG Based Emotion Classification Mechanism in BCI

Kaur, Barjinder Pal; Singh, Dinesh; Roy, Partha Pratim

doi:10.1016/j.procs.2018.05.087

Cited by 64 publications

(30 citation statements)

References 10 publications

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“…For the evaluation of human emotions, brains response to various stimuli are usually measured and analyzed by five frequency ranges from EEG signals, namely: delta, theta, alpha, beta and gamma. These band waves are omnipresent in different parts of the brain [45,46] and are related to various emotional states (Table 1). Table 1.…”

Section: Sensors 2020 20 X For Peer Review 4 Of 41mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

367

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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Section: Sensors 2020 20 X For Peer Review 4 Of 41mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

367

170

View full text Add to dashboard Cite

show abstract

“…Many researchers have attempted to categorize and detect everyday human emotions. It is proposed in [ 8 ] that features extracted can be classified using a support vector machine (SVM) with a Radial Basis Function (RBF) with 60% accuracy. Reference [ 9 ] extracted features from the EEG signal using the Dual-Tree Continuous Wavelet Transform (DT-CWT) based on time, frequency and nonlinear analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Asghar

Khan

Rizwan

et al. 2020

Sensors

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Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.

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“…It is also preferred for its ease of use, and for offering higher success rate for classification. The SVMlight [41] is a special purpose solver for SVM optimization problems. In this paper, SVM light is used as a classifier.…”

Section: Classificationmentioning

confidence: 99%

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

Ghumman,

Singh

2020

IJEAT

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Brain-computer interface (BCI) has emerged as a popular research domain in recent years. The use of electroencephalography (EEG) signals for motor imagery (MI) based BCI has gained widespread attention. The first step in its implementation is to fetch EEG signals from scalp of human subject. The preprocessing of EEG signals is done before applying feature extraction, selection and classification techniques as main steps of signal processing. In preprocessing stage, artifacts are removed from raw brain signals before these are input to next stage of feature extraction. Subsequently classifier algorithms are used to classify selected features into intended MI tasks. The major challenge in a BCI systems is to improve classification accuracy of a BCI system. In this paper, an approach based on Support Vector Machine (SVM), is proposed for signal classification to improve accuracy of the BCI system. The parameters of kernel are varied to attain improvement in classification accuracy. Independent component analysis (ICA) technique is used for preprocessing and filter bank common spatial pattern (FBCSP) for feature extraction and selection. The proposed approach is evaluated on data set 2a of BCI Competition IV by using 5-fold crossvalidation procedure. Results show that it performs better in terms of classification accuracy, as compared to other methods reported in literature.

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EEG Based Emotion Classification Mechanism in BCI

Cited by 64 publications

References 10 publications

Human Emotion Recognition: Review of Sensors and Methods

Human Emotion Recognition: Review of Sensors and Methods

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

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