Correlation and Relief Attribute Rank-based Feature Selection Methods for Detection of Alcoholic Disorder Using Electroencephalogram Signals

Kumari, Nandini; Anwar, Shamama; Bhattacherjee, Vandana

doi:10.1080/03772063.2020.1780166

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…For example, Kar et al [29] developed a feature selection method based on univariate feature selection techniques, extracting sample feature subsets from four different filter methods to accurately identify classifier models for distinguishing between epileptic and non-epileptic conditions. Kumari et al [30] proposed a Correlation-based and Relief attribute rank-based feature selection method to extract features from EEG signals, which effectively discriminated alcoholic groups and demonstrated excellent performance on the LS-SVM classifier. However, the filter method ignores the relationships between the features, making it unable to find the potential dependencies between the features, and the computational efficiency decreases as the number of features increases.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

Duan,

Sun,

Yang

et al. 2024

Preprint

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Electroencephalogram (EEG) plays a significant role in emotion recognition because it contains abundant information. However, due to the highly correlated EEG channels, a lot of redundant EEG features exist, which not only potentially degrade the emotion recognition accuracy, but also bring high computational cost. To address this challenge, this paper proposes an adaptive matrix-based evolutionary computation (MEC) framework to select as a small number of informative EEG features as possible for effective emotion recognition. Unlike most existing EC algorithms that utilize vector-based operations, this framework leverages matrix-based operations to reduce feature redundancy and improve classification accuracy by dynamically adjusting the feature subset size according to the characteristics of the dataset. In such a way, the selection efficiency is largely improved. To verify the effectiveness and efficiency of this framework, the classical Genetic Algorithm (GA), the typical Particle Swarm Optimization (PSO) algorithm, and the classical Differential Evolution (DE) algorithm, are respectively embedded into this framework for EEG feature selection, and then evaluated on three widely used public EEG datasets for emotion recognition. Compared with several state-of-the-art EEG feature selection algorithms, the devised framework is much more effective in terms of the classification accuracy, the feature subset optimization, and the computational efficiency. In addition, the experimental results further reveal that the selected feature subsets are very different for different genders. This indicates the demand of gender-sensitive EEG feature selection for emotion recognition.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

Duan,

Sun,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…By performing this process iteratively, the original set of features is condensed into a smaller bunch with less bivariate collinearity. Kumari et al [ 33 ] employed correlation-based feature selection to identify people with alcoholic disorder using recorded brain activity signals. Mitra et al [ 34 ] conducted a correlation-based feature selection study to investigate and classify different types of arrhythmia from ECG signals.…”

Section: Related Workmentioning

confidence: 99%

Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models

Balakarthikeyan

Jais

Vijayarangan

et al. 2023

Sensors

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Wearable Heart Rate monitors are used in sports to provide physiological insights into athletes’ well-being and performance. Their unobtrusive nature and ability to provide reliable heart rate measurements facilitate the estimation of cardiorespiratory fitness of athletes, as quantified by maximum consumption of oxygen uptake. Previous studies have employed data-driven models which use heart rate information to estimate the cardiorespiratory fitness of athletes. This signifies the physiological relevance of heart rate and heart rate variability for the estimation of maximal oxygen uptake. In this work, the heart rate variability features that were extracted from both exercise and recovery segments were fed to three different Machine Learning models to estimate maximal oxygen uptake of 856 athletes performing Graded Exercise Testing. A total of 101 features from exercise and 30 features from recovery segments were given as input to three feature selection methods to avoid overfitting of the models and to obtain relevant features. This resulted in the increase of model’s accuracy by 5.7% for exercise and 4.3% for recovery. Further, post-modelling analysis was performed to remove the deviant points in two cases, initially in both training and testing and then only in training set, using k-Nearest Neighbour. In the former case, the removal of deviant points led to a reduction of 19.3% and 18.0% in overall estimation error for exercise and recovery, respectively. In the latter case, which mimicked the real-world scenario, the average R value of the models was observed to be 0.72 and 0.70 for exercise and recovery, respectively. From the above experimental approach, the utility of heart rate variability to estimate maximal oxygen uptake of large population of athletes was validated. Additionally, the proposed work contributes to the utility of cardiorespiratory fitness assessment of athletes through wearable heart rate monitors.

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A Comparative Analysis of Machine and Deep Learning Techniques for EEG Evoked Emotion Classification

Kumari

Anwar

Bhattacherjee

2022

Wireless Pers Commun

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Correlation and Relief Attribute Rank-based Feature Selection Methods for Detection of Alcoholic Disorder Using Electroencephalogram Signals

Cited by 3 publications

References 49 publications

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

An Adaptive Matrix-Based Evolutionary Computation Framework for EEG Feature Selection

Heart Rate Variability Based Estimation of Maximal Oxygen Uptake in Athletes Using Supervised Regression Models

A Comparative Analysis of Machine and Deep Learning Techniques for EEG Evoked Emotion Classification

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