Emotion Analysis Through EEG and Peripheral Physiological Signals Using KNN Classifier

Shukla, Shourya; Chaurasiya, Rahul Kumar

doi:10.1007/978-3-030-00665-5_11

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Chung and Yoon [ 31 ] have used a weighted-log-posterior function for the Bayes classifier and reported an accuracy for valence and arousal classification of 66.6% and 66.4% for two classes and 53.4% and 51.0% for three classes, respectively. Shukla and Chaurasiya [ 167 ] have classified emotional states by using emotional dimension in the valence-arousal plane and reported that KNN outperformed other classifiers with an average accuracy of 87.1%. Three studies have analyzed differences in the performance of PCA in extracting desired features with different ML and DL classifiers.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

“… 21 channels Own database Entropy Dendrogram-SVM Accuracy = 92 [ 55 ] 2018 MWL 8 subj. 14 channels Own database PCA KNN SVM LR DT Accuracy = 70.6 [ 167 ] 2018 ER 32 subj. 32 channels DEAP database DWT KNN Accuracy = 87.1 [ 261 ] 2020 MI 9 subj.…”

Section: Table A1mentioning

confidence: 99%

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

et al. 2021

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“…This value is necessary to facilitate the achievement of classification results from the number of closest neighbors. When there is a class with the most neighbors, the test data will get that class result [27].…”

Section: K-nearest Neighbormentioning

confidence: 99%

Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor

Yudhana¹,

Muslim²,

Wati³

et al. 2020

Adv. sci. technol. eng. syst. j.

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“…For example, DL can group Alzheimer's disease and will support analysts and clinicians in diagnosing the brain disease with greater efficiency. This research work incorporates the convolutional neural networks, which is a feed forward network [9] and is widely used in the field of image recognition. In this research, ADNI dataset which contains the fMRI images of Alzheimer's disease patients undergoes conversion from 3D image to comma-separated value (CSV) dataset, which included a total of 2652 rows and 4097 columns data entries of two classes, i.e.…”

Section: Introductionmentioning

confidence: 99%

Early Diagnosis of Alzheimer’s Disease Using ACO Optimized Deep CNN Classifier

Singh

Janghel

2021

Smart Innovation, Systems and Technologies

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Alzheimer's disease (AD) is a human brain disease that remains as a common cause of dementia, which occurs mainly in middle-aged or grown-up individuals. AD results in cognitive decline and memory loss. AD is caused by the decomposition of plaques around the nerves of brains or around the brain cells, where the brain cells get neurofibrillary tangled and result in various instability and mental illness. AD is a chronic and irreversible disease; the reasons and disease identification are still not known, but research says it can be identified during the early stages. In spite of that fact, this research work has proposed a computer-aided Alzheimer's classification method that will classify the class of an image either in normal class or demented class. The method uses the hybrid strategy of ant colony optimization (ACO) and feed forward convolutional neural network (CNN or ConvNet); however, identifying the architecture of CNN requires lots of expertise and is time-consuming. Henceforth, this research work has used the bio-inspired optimization strategy, which will identify the optimal combination of hyper-parameters, i.e. it recommends the configuration for the CNN model, and with that, configuration of hyper-parameters with the CNN model is trained with the training dataset, and CNN performs feature extraction alongside classification for arranging the gatherings possibly, when the model undergoes validation, where the performance metric of the model is evaluated and to identify whether the validating images are falling in the category of normal class (i.e. non-demented) or Alzheimer's class (i.e. demented class) with good results or not, the classification error is measured during this phase and is backpropagated to ACO optimizer, iteratively ACO is used to minimize the classification error by tuning the hyper-parameters, and after few iterations, a CNN architecture with optimal hyper-parameters combinations is obtained to result in least classification errors. The method was applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which constitutes the fMRI images of Alzheimer's affected patients and resulted in developing efficient and state-of-the-art method for the classification of Alzheimer's disease. The proposed method performance metrics were recorded

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Emotion Analysis Through EEG and Peripheral Physiological Signals Using KNN Classifier

Cited by 6 publications

References 17 publications

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

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

Human Emotion Recognition Based on EEG Signal Using Fast Fourier Transform and K-Nearest Neighbor

Early Diagnosis of Alzheimer’s Disease Using ACO Optimized Deep CNN Classifier

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