Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction

Liu, Jingxin; Meng, Hongying; Li, Maozhen; Zhang, Fan; Qin, Rui; Nandi, Asoke K.

doi:10.1002/cpe.4446

Cited by 48 publications

(25 citation statements)

References 39 publications

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“…With many features, the size of the learning set required for modeling must be proportionally large, which takes a long time to categorize. Accordingly, various manifold learning approaches such as PCA (principal components analysis), maximal revision minimum redundancy (mRMR) [ 9 ], and various feature space reduction techniques such as selecting features using F-scores have been used to address the issue [ 10 ]. The existing stress analysis methods lack in an effective features dimensionality reduction technique to improve performance of a subsequent classifier.…”

Section: Introductionmentioning

confidence: 99%

Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

Shon

Park

et al. 2018

IJERPH

112

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In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and k-nearest neighbor (k-NN) classifier are used to identify stress in human beings by analyzing electro-encephalography (EEG) signals. GA is incorporated in the stress analysis pipeline to effectively select subset of features that are suitable to enhance the performance of the k-NN classifier. The performance of the proposed method is evaluated using the Database for Emotion Analysis using Physiological Signals (DEAP), which is a public EEG dataset. A feature set is extracted in 32 EEG channels, which consists of statistical features, Hjorth parameters, band power, and frontal alpha asymmetry. The selected features through GA are used as input to the k-NN classifier to distinguish whether each EEG datapoint represents a stress state. To further consolidate, the effectiveness of the proposed method is compared with that of a state-of-the-art principle component analysis (PCA) method. Experimental results show that the proposed GA-based method outperforms PCA, with GA demonstrating 71.76% classification accuracy compared with 65.3% for PCA. Thus, it can be concluded that the proposed method can be effectively used for stress analysis with high classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

Shon

Park

et al. 2018

IJERPH

112

View full text Add to dashboard Cite

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“…The original article presented the DEAP database reported a 62.0 % for low-high arousal and a 57.6% for lowhigh valence classification. A recent study reported an improvement with 74.3 % for low-high arousal and 77.2% for low-high valence classification tasks [12]. Another study reported the first results from a static connectivity analysis incorporating the richness of spatiotemporal EEG brain activity from 1 min into a single functional brain network.…”

Section: Introductionmentioning

confidence: 99%

A Multiplex Connectivity Map of Valence-Arousal Emotional Model

2020

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A high number of studies have already demonstrated an electroencephalography (EEG)-based emotion recognition system with moderate results. Emotions are classified into discrete and dimensional models. We focused on the latter that incorporates valence and arousal dimensions. The mainstream methodology is the extraction of univariate measures derived from EEG activity from various frequencies classifying trials into low/high valence and arousal levels. Here, we evaluated brain connectivity within and between brain frequencies under the multiplexity framework. We analyzed an EEG database called DEAP that contains EEG responses to video stimuli and users' emotional self-assessments. We adopted a dynamic functional connectivity analysis under the notion of our dominant coupling model (DoCM). DoCM detects the dominant coupling mode per pair of EEG sensors, which can be either within frequencies coupling (intra) or between frequencies coupling (cross-frequency). DoCM revealed an integrated dynamic functional connectivity graph (IDFCG) that keeps both the strength and the preferred dominant coupling mode. We aimed to create a connectomic mapping of valence-arousal map via employing features derive from IDFCG. Our results outperformed previous findings succeeding to predict in a high accuracy participants' ratings in valence and arousal dimensions based on a flexibility index of dominant coupling modes.

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“…It can analyse the main influencing factors from multiple contexts, reveal the essence of entities, and simplify complex problems. Liu et al proposed a hybrid dimension featurereduction scheme using 14 different features extracted from EEG recordings [24]. To reorder the combined features into max-relevance with the labels and min-redundancy of each feature, maximum relevance minimum redundancy (mRMR) was applied.…”

Section: Introductionmentioning

confidence: 99%

A WPCA-Based Method for Detecting Fatigue Driving From EEG-Based Internet of Vehicles System

Dong

Gao

et al. 2019

IEEE Access

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Fatigue driving is the main cause of traffic accidents. Analysis of electroencephalogram (EEG) signals has attracted wide attention for identifying fatigue driving. With the development of the Internet of Vehicles (IoV), we hope to establish an EEG-based IoV traffic management system to improve traffic safety. In the proposed system, real-time diagnosis is a significant factor, and improvement of the detection speed is our main concern. EEG signals generate a large amount of spatially oriented data over a relatively short duration; hence, their dimension needs to be reduced effectively before being analysed. We proposes a feature reduction method, based on a novel weighted principal component analysis (WPCA) algorithm for EEG signals. First, the EEG features are extracted by an autoregressive (AR) model. Second, we calculate the influence of different features on the classified performance of fatigue state. The accuracy reduction values of different features are normalised as the weights of the features. Finally, these weights are assigned to the WPCA to reduce the EEG features. To verify the effectiveness of the algorithm, we carried out a simulated driving experiment involving eight participants. For comparison, power spectral density and differential entropy models were also introduced to extract EEG features. Support Vector Machine was adopted as a classifier to establish a fatigue driving classification experiment. The experimental results show that the WPCA method can effectively reduce the feature dimension of different EEG feature extraction methods, speed up calculations, and achieve a much higher classification accuracy of fatigue driving.

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Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction

Cited by 48 publications

References 39 publications

Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals

A Multiplex Connectivity Map of Valence-Arousal Emotional Model

A WPCA-Based Method for Detecting Fatigue Driving From EEG-Based Internet of Vehicles System

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