ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal

Khosrowabadi, Reza; Quek, Chai; Ang, Kai Keng; Wahab, Abdul

doi:10.1109/tnnls.2013.2280271

Cited by 90 publications

(59 citation statements)

References 56 publications

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“…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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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: 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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“…The subject is seated in the experimental room in front of the system and asked to fill the some set of questions , after finishing that process, some electrode has to be placed on the scalp. The EEG recorded using BIM EC fro m Brain maker BV [1]. The BIM EC has the reference channel with eight EEG sample signal with 250Hz.…”

Section: Eeg Datamentioning

confidence: 99%

“…EEG power has decreased during the sad emotion and increased during the happiness. The region that shows the difference between sadness and happiness is the frontal pole with left CBF being h igher during sadness and lower during happiness [1]. So that we can identify the positive and negative emotions from the past experience.…”

Section: Introductionmentioning

confidence: 98%

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A Survey On Emotion Classification From Eeg Signal Using Various Techniques and Performance Analysis

Sreeshakthy¹,

Preethi²,

A³

2016

IJITCS

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“…Sci. 2016, 6, 142 2 of 19 emotion recognition [16,17], and steady-state visually evoked potential (SSVEP)-based BCIs [18,19]. In this paper, EEG is adopted as the input of the developed BCI.…”

Section: Introductionmentioning

confidence: 99%

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

Liu

Wang

2016

Applied Sciences

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This paper presents a novel brain-computer interface (BCI)-based healthcare control system, which is based on steady-state visually evoked potential (SSVEP) and P300 of electroencephalography (EEG) signals. The proposed system is composed of two modes, a brain switching mode and a healthcare function selection mode. The switching mode can detect whether a user has the intent to activate the function selection mode by detecting SSVEP in an ongoing EEG. During the function selection mode, the user is able to select any functions that he/she wants to activate through a healthcare control panel, and the function selection is done by detecting P300 in the user's EEG signals. The panel provides 25 functions representing 25 frequently performed activities of daily life. Therefore, users with severe motor disabilities can activate the system and any functions in a self-paced manner, achieving the goal of autonomous healthcare. To achieve high P300 detection accuracy, a novel P300 detector based on kernel Fisher's discriminant analysis (kernel FDA) and support vector machine (SVM) is also proposed. Experimental results, carried out on five subjects, show that the proposed BCI system achieves high SSVEP detection (93%) and high P300 detection (95.5%) accuracies, meaning that the switching mode has a high sensitivity, and the function selection mode has the ability to accurately detect the functions that the users want to trigger. More important, only three electrodes (Oz, Cz, and Pz) are required to measure EEG signals, enabling the system to have good usability in practical use.

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ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal

Abstract: Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG.

Cited by 90 publications

References 56 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

A Survey On Emotion Classification From Eeg Signal Using Various Techniques and Performance Analysis

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

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