Enhancing Text Using Emotion Detected from EEG Signals

Gupta, Akash; Sahu, Harsh; Nanecha, Nihal; Kumar, Pradeep; Roy, Partha Pratim; Chang, Victor

doi:10.1007/s10723-018-9462-2

Cited by 23 publications

(9 citation statements)

References 38 publications

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“…Hatamikia et al [ 44 ] found that spectral entropy outperformed the Petrosian and Katz fractal dimensions for emotion recognition. Gupta et al [ 35 ] used SVD entropy as part of a set of features to classify discrete emotion for short movies. Equations are as given in [ 72 ].…”

Section: Methodsmentioning

confidence: 99%

“…Various machine learning techniques have been investigated to improve the performance of emotion recognition and other health related systems (e.g., stroke management) using EEG [ 30 , 31 ]. Many classical classifiers have been proposed, including support vector machine (SVM) [ 32 , 33 , 34 ], linear discriminant analysis (LDA) [ 33 , 34 ], k-nearest neighbours (kNN) [ 33 ], random forest (RF) [ 35 , 36 ], Naïve Bayes (NB) [ 32 , 36 ], extreme gradient boosting (XGB) [ 32 ], and decision trees [ 33 , 36 ]. The performances achieved by these classifiers vary, and differences are often overshadowed by the impact of the features chosen as part of these models.…”

Section: Related Workmentioning

confidence: 99%

“…To extract these features, frequency (Freq) domain techniques such as power spectral density (PSD) [ 40 , 41 ] and asymmetry (Asymm) [ 20 ] between electrodes have been proposed. Time-domain features can also be extracted, such as high order crossings [ 42 ] and Hjorth parameters [ 35 , 43 ]. Non-linear methods can be used to extract various entropy measures [ 44 ] and fractal dimensions [ 44 , 45 ], and more recently, features have been learned automatically using deep learning approaches [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

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Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition

Bendrich

Kumar

Scheme

2022

Sensors

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The monitoring of emotional state is important in the prevention and management of mental health problems and is increasingly being used to support affective computing. As such, researchers are exploring various modalities from which emotion can be inferred, such as through facial images or via electroencephalography (EEG) signals. Current research commonly investigates the performance of machine-learning-based emotion recognition systems by exposing users to stimuli that are assumed to elicit a single unchanging emotional response. Moreover, in order to demonstrate better results, many models are tested in evaluation frameworks that do not reflect realistic real-world implementations. Consequently, in this paper, we explore the design of EEG-based emotion recognition systems using longer, variable stimuli using the publicly available AMIGOS dataset. Feature engineering and selection results are evaluated across four different cross-validation frameworks, including versions of leave-one-movie-out (testing with a known user, but a previously unseen movie), leave-one-person-out (testing with a known movie, but a previously unseen person), and leave-one-person-and-movie-out (testing on both a new user and new movie). Results of feature selection lead to a 13% absolute improvement over comparable previously reported studies, and demonstrate the importance of evaluation framework on the design and performance of EEG-based emotion recognition systems.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition

Bendrich

Kumar

Scheme

2022

Sensors

Self Cite

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“…As one of the early ERC methods, Poria et al 13 proposed bidirectional contextual LSTM (bc-LSTM) to capture contextual information from their surroundings. Gupta et al 31 proposed a method to detect and enhance the emotion of the user on social media by analyzing the electroencephalogram signals from the brain. Hazarika et al 32 proposed a conversational memory network for dyadic dialogs utilizing speaker-specific context modeling.…”

Section: Related Workmentioning

confidence: 99%

Residual-Based Graph Convolutional Network for Emotion Recognition in Conversation for Smart Internet of Things

2021

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Recently, emotion recognition in conversation (ERC) has become more crucial in the development of diverse internet of things devices, especially closely connected with users. The majority of deep learning-based methods for ERC combine the multilayer, bidirectional, recurrent feature extractor and the attention module to extract sequential features. In addition to this, the latest model utilizes speaker information and the relationship between utterances through the graph network. However, before the input is fed into the bidirectional recurrent module, detailed intrautterance features should be obtained without variation of characteristics. In this article, we propose a residual-based graph convolution network (RGCN) and a new loss function. Our RGCN contains the residual network (ResNet)-based, intrautterance feature extractor and the GCN-based, interutterance feature extractor to fully exploit the intra-inter informative features. In the intrautterance feature extractor based on ResNet, the elaborate context feature for each independent utterance can be produced. Then, the condensed feature can be obtained through an additional GCN-based, interutterance feature extractor with the neighboring associated features for a conversation. The proposed loss function reflects the edge weight to improve effectiveness. Experimental results demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods.

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“…Here, EEG signals are typically obtained using the international 10-20 system, which is an internationally recognized method for describing and applying the location of the scalp electrode and underlying area of the cerebral cortex [ 40 ]. Moreover, the “10” and “20” in the international 10-20 system refer to the fact that the actual distance between the adjacent electrodes is either 10% or 20% of the total left-right or front-back distance of the skull.…”

Section: Proposed Approachmentioning

confidence: 99%

Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network

Cho

Hwang

2020

Sensors

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Emotion recognition plays an important role in the field of human–computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility. Deep neural network (DNN) approaches using an EEG for emotion recognition have recently shown remarkable improvement in terms of their recognition accuracy. However, most studies in this field still require a separate process for extracting handcrafted features despite the ability of a DNN to extract meaningful features by itself. In this paper, we propose a novel method for recognizing an emotion based on the use of three-dimensional convolutional neural networks (3D CNNs), with an efficient representation of the spatio-temporal representations of EEG signals. First, we spatially reconstruct raw EEG signals represented as stacks of one-dimensional (1D) time series data to two-dimensional (2D) EEG frames according to the original electrode position. We then represent a 3D EEG stream by concatenating the 2D EEG frames to the time axis. These 3D reconstructions of the raw EEG signals can be efficiently combined with 3D CNNs, which have shown a remarkable feature representation from spatio-temporal data. Herein, we demonstrate the accuracy of the emotional classification of the proposed method through extensive experiments on the DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) dataset. Experimental results show that the proposed method achieves a classification accuracy of 99.11%, 99.74%, and 99.73% in the binary classification of valence and arousal, and, in four-class classification, respectively. We investigate the spatio-temporal effectiveness of the proposed method by comparing it to several types of input methods with 2D/3D CNN. We then verify the best performing shape of both the kernel and input data experimentally. We verify that an efficient representation of an EEG and a network that fully takes advantage of the data characteristics can outperform methods that apply handcrafted features.

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Enhancing Text Using Emotion Detected from EEG Signals

Cited by 23 publications

References 38 publications

Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition

Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition

Residual-Based Graph Convolutional Network for Emotion Recognition in Conversation for Smart Internet of Things

Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network

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