EEG-Based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor

Putra, Agfianto Eko; Atmaji, Catur; Ghaleb, Fajrul

doi:10.1109/icstc.2018.8528652

Cited by 11 publications

(6 citation statements)

References 7 publications

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“…In Kimmatkar et al [32], the classification accuracy using EEG signal to classify human emotions had reached 98.67%. Few studies focused on multi-classification of emotions such as Putra et al who achieved an average accuracy around 60-70% [33]. In this paper, a neural feedback experiment with three experimental stages was designed to study anxiety issue.…”

Section: Discussionmentioning

confidence: 99%

EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System

Chen

Belkacem

et al. 2021

J. Med. Biol. Eng.

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Purpose Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals. Methods We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects' mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups. Results After processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48 ± 1.20% and 88.60 ± 1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31 ± 1.97% and for anxiety subjects is 87.18 ± 3.51%. Conclusions The results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.

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Section: Discussionmentioning

confidence: 99%

EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System

Chen

Belkacem

et al. 2021

J. Med. Biol. Eng.

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“…Wavelet transform is a popular time–frequency (TF) decomposition technique that divides the EEG signal in several approximation and details levels of wavelet coefficients corresponding to various EEG frequency ranges, while conserving the time information of the signal. Previous studies have used wavelet analysis to measure the EEG TF distribution related to emotions [ 13 , 24 , 25 , 26 ]. Here, six-level continuous wavelet transform ( CWT ) was applied using the Morlet window function to obtain wavelet coefficients of EEG bands.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

Yuvaraj

Thangavel

Thomas

et al. 2023

Sensors

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Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal. The classification accuracy of five EEG feature sets were investigated, including statistical features, fractal dimension (FD), Hjorth parameters, higher order spectra (HOS), and those derived using wavelet analysis. Performance was evaluated using two classifier methods, support vector machine (SVM) and classification and regression tree (CART), across five independent and publicly available datasets linking EEG to emotional states: MAHNOB-HCI, DEAP, SEED, AMIGOS, and DREAMER. The FD-CART feature-classification method attained the best mean classification accuracy for valence (85.06%) and arousal (84.55%) across the five datasets. The stability of these findings across the five different datasets also indicate that FD features derived from EEG data are reliable for emotion recognition. The results may lead to the possible development of an online feature extraction framework, thereby enabling the development of an EEG-based emotion recognition system in real time.

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“…Recent papers comparing subject dependent and independent approaches showed that the former consistently gave 5–30% higher performance depending on the implemented approach. Such results are mainly due to the discrepancy between subjects related to how they feel and express their emotions [ 75 ]. For example, Nath et al [ 73 ] have observed that EEG signals from a specific subject were somewhat similar yet significantly varied across different subjects, even when the same stimulus was considered.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Abdel-Hamid

2023

Sensors

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Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3–22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.

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EEG-Based Emotion Classification Using Wavelet Decomposition and K-Nearest Neighbor

Cited by 11 publications

References 7 publications

EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System

EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

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