Emotion Discrimination Using Spatially Compact Regions of Interest Extracted from Imaging EEG Activity

Padilla-Buriticá, Jorge Ivan; Martínez-Vargas, Juan David; Castellanos-Domínguez, Germán

doi:10.3389/fncom.2016.00055

Cited by 30 publications

(21 citation statements)

References 39 publications

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“…Besides, existing signals are not enough for high accuracy feature extraction. Several approaches introduce more features in different analysis domains to capture extra information about the state of the brain [107,117,200,203,213,216,224]. Consequently, feature extraction is one of the major challenges in designing BCI systems; it is determined based on the features and on the appropriate transformation.…”

Section: Eeg Correlates Of Emotion (Signals)mentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Eeg Correlates Of Emotion (Signals)mentioning

confidence: 99%

“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”

Section: Monitoringmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…Several studies have been carried out in order to investigate these methods with the aim of removing artifacts [10][11][12][15][16][17]. Different articles have come to a conclusion that Independent Component Analysis (ICA), introduced as a noise suppression tool for the first time in [18], is the most robust method in artifact elimination but is not very time fast. Among different BSS based methods, second order blind identification (SOBI) is reportedly the most effective one.…”

Section: Introductionmentioning

confidence: 99%

EEG Source Identification through Phase Space Reconstruction and Complex Networks

Soroush¹

2020

Preprint

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Artifact elimination has become an inseparable part while processing electroencephalogram (EEG) in most brain computer interface (BCI) applications. Scientists have tried to introduce effective and efficient methods which can remove artifacts and also reserve desire information pertaining to brain activity. Blind source separation (BSS) methods have been receiving a great deal of attention in recent decades since they are considered routine and standard signal processing tools and are commonly used to eliminate artifacts and noise. Most studies, mainly EEG-related ones, apply BSS methods in preprocessing sections to achieve better results. On the other hand, BSS methods should be followed by a classifier in order to identify artifactual sources and remove them in next steps. Therefore, artifact identification is always a challenging problem while employing BSS methods. Additionally, removing all detected artifactual components leads to loss of information since some desire information related to neural activity leaks to these sources. So, an approach should be employed to suppress the artifacts and reserve neural activity. In this study, a new hybrid method is proposed to automatically separate and identify electroencephalogram (EEG) sources with the aim of classifying and removing artifacts. Automated source identification is still a challenge. Researchers have always made efforts to propose precise, fast and automated source verification methods. Reliable source identification has always been of great importance. This paper addresses blind source separation based on second order blind identification (SOBI) as it is reportedly one of the most effective methods in EEG source separation problems. Then a new method for source verification is introduced which takes advantage of components phase spaces and their dynamics. A new state space called angle space (AS) is introduced and features are extracted based on the angle plot (AP) and Poincare planes. Identified artifactual sources are eliminated using stationary wavelet transform (SWT). Simulated, semi-simulated and real EEG signals are employed to evaluate the proposed method. Different simulations are performed and performance indices are reported. Results show that the proposed method outperforms most recent studies in this subject.

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“…Brain activity can be characterized by various signal modalities, such as invasive ElectroCorticoGraphy (ECoG) (Miller et al, 2010 ; Hiremath et al, 2015 ), non-invasive electroencephalogram (EEG) (Lazarou et al, 2018 ), the functional Magnetic Resonance Imaging (fMRI) (Cohen et al, 2014 ), and the functional Near-Infrared Spectroscopy (fNIRS) (Naseer and Hong, 2015 ). Due to its manageability, easy capture, high time resolution and relative cost effectiveness, the EEG signal has been widely adopted for substantial BCI applications, such as remote quadcopter control (Lin and Jiang, 2015 ), motion rehabilitation (Xu et al, 2011 ; Zhao et al, 2016 ), biometric authentication (Palaniappan, 2008 ), and emotions prediction (Padilla-Buritica et al, 2016 ). Currently, the electrophysiological brain patterns used in EEG-based BCI systems are mainly Steady-State Visual Evoked Potentials (SSVEPs) (Chen et al, 2015 ; Zhang et al, 2015 ; Zhao et al, 2016 ; Nakanishi et al, 2018 ), P300 (Cavrini et al, 2016 ), sensorimotor rhythms (SMRs) (Yuan and He, 2014 ; He et al, 2015 ), and motion-related cortical potential (MRCP, one kind of a slow cortical potential) (Karimi et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network

et al. 2018

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High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application.

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Emotion Discrimination Using Spatially Compact Regions of Interest Extracted from Imaging EEG Activity

Cited by 30 publications

References 39 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

EEG Source Identification through Phase Space Reconstruction and Complex Networks

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network

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