Design of Intelligent EEG System for Human Emotion Recognition with Convolutional Neural Network

Wang, Kai-Yen; Ho, Yun-Lung; Huang, Yuan Ming; Fang, Wai-Chi

doi:10.1109/aicas.2019.8771581

Cited by 26 publications

(11 citation statements)

References 9 publications

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“…Until 2017 (Al-Nafjan et al, 2017 ), the most adopted classification approach was SVM, often in conjunction with power spectral density (PSD)-based features. However, deep learning approaches are standing out also in this domain, showing the potential to outperform traditional ML techniques (Zheng and Lu, 2015 ; Li et al, 2016 ; Wang et al, 2019 ). However, to the best of our knowledge, few works have addressed EEG ER in real HRI scenarios.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020

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A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

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Section: State Of the Artmentioning

confidence: 99%

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020

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“…This model was fused with the multichannel intelligent human emotion detection system. Mapping of emotions can be done using three-dimensional vectors: Valence, Arousal, and Dominance (VAD) [ 30 ].…”

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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“…Since the birth of the world, the research on convolutional neural networks has also made great progress. At present, there are hundreds of convolutional neural networks, among which the representative ones are convolutional neural networks, pattern recognition, music element processing, nance, in the elds of voice intelligent creation, and music element intelligent creation [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Construction of Music Intelligent Creation Model Based on Convolutional Neural Network

Jing

2022

Computational Intelligence and Neuroscience

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The application of machine learning technology to intelligent music creation has become a very important field in music creation. The main current research on music intelligent creation methods uses fixed coding steps in audio data, which lead to weak feature expression ability. Based on convolutional neural network theory, this paper proposes a deep music intelligent creation method. The model uses a convolutional recurrent neural network to generate an effective hash code, first preprocesses the music signal to obtain a Mel spectrogram, and then inputs it into a pretrained CNN to extract from its convolutional layers. The network space details and the semantic information of musical symbols are used to construct the feature map sequence using selection strategy for the feature map of each convolutional layer, so as to solve the problem of high data feature dimension and poor recognition performance. In the simulation process, the Mel cepstral coefficient method (MFCC) was used to extract the features of four different music signals, and the features that could represent each signal were extracted through the convolutional neural network, and the continuous signals were discretized and reduced. The experimental results show that the high-dimensional music data are dimensionally reduced at the data level. After the data are compressed, the correct rate of intelligent creation is as high as 98%, and the characteristic signal distortion rate is reduced to 5% below, effectively improving the algorithm performance and the ability to create music intelligently.

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Design of Intelligent EEG System for Human Emotion Recognition with Convolutional Neural Network

Cited by 26 publications

References 9 publications

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

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

Construction of Music Intelligent Creation Model Based on Convolutional Neural Network

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