An approach to emotion Recognition in Single-channel EEG Signals using Stationary Wavelet Transform

The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing.

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Automated identification of insomnia using optimal bi-orthogonal wavelet transform technique with single-channel EEG signals

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.107078 ◽

2021 ◽

pp. 107078

Author(s):

Manish Sharma ◽

Virendra Patel ◽

U. Rajendra Acharya

Keyword(s):

Wavelet Transform ◽

Single Channel ◽

Eeg Signals ◽

Automated Identification ◽

Orthogonal Wavelet ◽

Orthogonal Wavelet Transform

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GameEmo-CapsNet: Emotion Recognition from Single-Channel EEG Signals Using the 1D Capsule Networks

Traitement du signal ◽

10.18280/ts.380612 ◽

2021 ◽

Vol 38 (6) ◽

pp. 1689-1698

Author(s):

Suat Toraman ◽

Ömer Osman Dursun

Keyword(s):

Emotion Recognition ◽

Facial Expressions ◽

Classification Accuracy ◽

Single Channel ◽

Visual Stimuli ◽

Classification Performance ◽

Eeg Signals ◽

Machine Learning Methods ◽

First Time ◽

Better Than

Human emotion recognition with machine learning methods through electroencephalographic (EEG) signals has become a highly interesting subject for researchers. Although it is simple to define emotions that can be expressed physically such as speech, facial expressions, and gestures, it is more difficult to define psychological emotions that are expressed internally. The most important stimuli in revealing inner emotions are aural and visual stimuli. In this study, EEG signals using both aural and visual stimuli were examined and emotions were evaluated in both binary and multi-class emotion recognitions models. A general emotion recognition model was proposed for non-subject-based classification. Unlike in previous studies, a subject-based testing was performed for the first time in the literature. Capsule Networks, a new neural network model, has been developed for binary and multi-class emotion recognition. In the proposed method, a novel fusion strategy was introduced for binary-class emotion recognition and the model was tested using the GAMEEMO dataset. Binary-class emotion recognition achieved a classification accuracy which was 10% better than the classification performance achieved in other studies in the literature. Based on these findings, we suggest that the proposed method will bring a different perspective to emotion recognition.

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An approach to emotion recognition in single-channel EEG signals: a mother child interaction

Journal of Physics Conference Series ◽

10.1088/1742-6596/705/1/012051 ◽

2016 ◽

Vol 705 ◽

pp. 012051 ◽

Cited By ~ 10

Author(s):

A Gómez ◽

L Quintero ◽

N López ◽

J Castro

Keyword(s):

Emotion Recognition ◽

Single Channel ◽

Eeg Signals ◽

Child Interaction ◽

Mother Child Interaction

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EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach

Sensors ◽

10.3390/s19235218 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5218 ◽

Cited By ~ 7

Author(s):

Muhammad Adeel Asghar ◽

Muhammad Jamil Khan ◽

Fawad ◽

Yasar Amin ◽

Muhammad Rizwan ◽

...

Keyword(s):

Emotion Recognition ◽

Single Channel ◽

Support Vector ◽

Data Sets ◽

Learning Technology ◽

Emotional States ◽

Eeg Signal ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Data Set

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D Spectrogram for each channel. To reduce the feature dimensionality, spatial, and temporal based, bag of deep features (BoDF) model is proposed. A series of vocabularies consisting of 10 cluster centers of each class is calculated using the k-means cluster algorithm. Lastly, the emotion of each subject is represented using the histogram of the vocabulary set collected from the raw-feature of a single channel. Features extracted from the proposed BoDF model have considerably smaller dimensions. The proposed model achieves better classification accuracy compared to the recently reported work when validated on SJTU SEED and DEAP data sets. For optimal classification performance, we use a support vector machine (SVM) and k-nearest neighbor (k-NN) to classify the extracted features for the different emotional states of the two data sets. The BoDF model achieves 93.8% accuracy in the SEED data set and 77.4% accuracy in the DEAP data set, which is more accurate compared to other state-of-the-art methods of human emotion recognition.

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Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2019.03.015 ◽

2019 ◽

Vol 173 ◽

pp. 157-165 ◽

Cited By ~ 14

Author(s):

Sachin Taran ◽

Varun Bajaj

Keyword(s):

Emotion Recognition ◽

Instantaneous Frequency ◽

Single Channel ◽

Eeg Signals ◽

Two Stage ◽

Filtering Method

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Discrete Wavelet Transform coefficients for emotion recognition from EEG signals

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/embc.2012.6346410 ◽

2012 ◽

Cited By ~ 21

Author(s):

R. E. J. Yohanes ◽

Wee Ser ◽

Guang-bin Huang

Keyword(s):

Wavelet Transform ◽

Emotion Recognition ◽

Discrete Wavelet Transform ◽

Discrete Wavelet ◽

Eeg Signals ◽

Transform Coefficients

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EEG Signal Analysis for Diagnosing Neurological Disorders Using Discrete Wavelet Transform and Intelligent Techniques

Sensors ◽

10.3390/s20092505 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2505 ◽

Cited By ~ 5

Author(s):

Fahd A. Alturki ◽

Khalil AlSharabi ◽

Akram M. Abdurraqeeb ◽

Majid Aljalal

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Single Channel ◽

Autism Spectrum ◽

Support Vector ◽

Discrete Wavelet ◽

Band Pass Filter ◽

Eeg Signal ◽

Brain Disorders ◽

Eeg Signals

Analysis of electroencephalogram (EEG) signals is essential because it is an efficient method to diagnose neurological brain disorders. In this work, a single system is developed to diagnose one or two neurological diseases at the same time (two-class mode and three-class mode). For this purpose, different EEG feature-extraction and classification techniques are investigated to aid in the accurate diagnosis of neurological brain disorders: epilepsy and autism spectrum disorder (ASD). Two different modes, single-channel and multi-channel, of EEG signals are analyzed for epilepsy and ASD. The independent components analysis (ICA) technique is used to remove the artifacts from EEG dataset. Then, the EEG dataset is segmented and filtered to remove noise and interference using an elliptic band-pass filter. Next, the EEG signal features are extracted from the filtered signal using a discrete wavelet transform (DWT) to decompose the filtered signal to its sub-bands delta, theta, alpha, beta and gamma. Subsequently, five statistical methods are used to extract features from the EEG sub-bands: the logarithmic band power (LBP), standard deviation, variance, kurtosis, and Shannon entropy (SE). Further, the features are fed into four different classifiers, linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural networks (ANNs), to classify the features corresponding to their classes. The combination of DWT with SE and LBP produces the highest accuracy among all the classifiers. The overall classification accuracy approaches 99.9% using SVM and 97% using ANN for the three-class single-channel and multi-channel modes, respectively.

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