Adaptive Tunable <i>Q</i> Wavelet Transform-Based Emotion Identification

Khare, Smith K.; Bajaj, Varun; Sinha, G. R.

doi:10.1109/tim.2020.3006611

Cited by 68 publications

(18 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, the studies that did not use this shared data set [ 65 , 218 , 219 , 225 , 226 , 227 , 228 , 229 , 230 ] all achieved higher classification performance by using SVM, KNN, and ANNs algorithms. For example, Seo et al [ 227 ] have compared different ML classifiers to classify boredom and non-boredom in 28 participants on the basis of the historical models of emotion and found that the KNN outperformed both RF and ANNs.…”

Section: Discussionmentioning

confidence: 94%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

show abstract

Section: Discussionmentioning

confidence: 94%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Also, majority voting was increased the obtained accuracy scores. In future works, the deep learning achievements will be investigated on the EEG rhythms [27][28][29]. Besides, the achievements of all EEG channels will be investigated in the deep learning fashion.…”

Section: Discussionmentioning

confidence: 99%

Two-stepped majority voting for efficient EEG-based emotion classification

et al. 2020

View full text Add to dashboard Cite

In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human–machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension-based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG-based emotion classification.

show abstract

“…The classification accuracy of GRNN is largely dependent on the accurate value of the spread factor. In this study, the spread factor is fixed to 1 after several experiments for the classification of focal and nonfocal EEG signals [ 1 , 77 , 78 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain

Sadiq

Akbari

Rehman

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

show abstract

Adaptive Tunable Q Wavelet Transform-Based Emotion Identification

Cited by 68 publications

References 41 publications

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Two-stepped majority voting for efficient EEG-based emotion classification

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain

Contact Info

Product

Resources

About