Effective Hybrid Method for the Detection and Rejection of Electrooculogram (EOG) and Power Line Noise Artefacts From Electroencephalogram (EEG) Mixtures

Ahmed, Mohammed; Qi, Deyu; AlShemmary, Ebtesam N.

doi:10.1109/access.2020.3036134

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…On the one hand, the physiological artifacts are mainly generated by the biological activity of the system’s user. For example, these artifacts are generated by the heartbeat, eye movement, muscle activation and user movement [ 26 , 27 , 28 ]. Fortunately, the methods to eliminate these artifacts are quite simple because they represent repeated morphology waves related to body member function that can be learned by the system during the training phase.…”

Section: Introductionmentioning

confidence: 99%

Embedded Brain Computer Interface: State-of-the-Art in Research

Belwafi

Gannouni

Aboalsamh

2021

Sensors

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There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.

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Section: Introductionmentioning

confidence: 99%

Embedded Brain Computer Interface: State-of-the-Art in Research

Belwafi

Gannouni

Aboalsamh

2021

Sensors

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“…Swarm intelligence-based (SI) heuristic search methods aim to investigate the behavior of a group of agents in self-organized communities, such as ants, bees, moths, and birds [1]. Recently, several SI algorithms, such as the Ant Colony Optimization (ACO) [28], Bee Optimization Algorithm (BeOA) [29], Moth-flame optimizer (MFO) [30], Multi-Verse Optimizer (MVO) [31], Butterfly Optimization algorithm (BOA) [32], Bat Algorithm (BAT) [33], Firefly Algorithm (FFA) [1], Grey Wolf Optimizer (GWO) [34], Moth Optimization Algorithm (MOA) [30], Whale Optimization Algorithm (WOA) [2] and Particle Swarm Optimization (PSO) [2,35] have been successfully utilized to discover the optimal feature subset. However, despite the excellent findings, most of these algorithms have a poor convergence rate and are entrapped in local optima [32].…”

Section: Introductionmentioning

confidence: 99%

Enhancing EEG-Based Mental Stress State Recognition using an Improved Hybrid Feature Selection Algorithm

Hag¹,

Handayani²,

Altalhi³

et al. 2021

Preprint

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Mental stress state recognition using electroencephalogram (EEG) signals for real-life applications needs a conventional wearable device. This requires an efficient number of EEG channels and an optimal feature set. The main objective of the study is to identify an optimal feature subset that can best discriminate mental stress states while enhancing the overall performance. Thus, multi-domain feature extraction methods were employed, namely, time domain, frequency domain, time-frequency domain, and network connectivity features, to form a large feature vector space. To avoid the computational complexity of high dimensional space, a hybrid feature selection (FS) method of minimum Redundancy Maximum Relevance with Particle Swarm Optimization and Support Vector Machine (mRMR-PSO-SVM) is proposed to remove noise, redundant, and irrelevant features and keep the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art heuristic methods. The proposed model has significantly reduced the features vector space by an average of 70% in comparison to the state-of-the-art methods while significantly increasing overall detection performance.

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“…Swarm intelligence-based (SI) heuristic search methods aim to investigate the behaviour of a group of agents in self-organized communities, such as ants, bees, moths, and birds [3]. Recently, several SI algorithms, such as the ant colony optimization (ACO) [34], bee optimization algorithm (BeOA) [35], moth-flame optimizer (MFO) [36], multi-verse optimizer (MVO) [37], butterfly optimization algorithm (BOA) [38], bat algorithm (BAT) [39], firefly algorithm (FFA) [3], grey wolf optimizer (GWO) [40], moth optimization algorithm (MOA) [36], whale optimization algorithm (WOA) [2], and particle swarm optimization (PSO) [2,41], have been successfully utilized to discover the optimal feature subset. However, despite the excellent findings, most of these algorithms have a poor convergence rate and are entrapped in local optima [38].…”

Section: Introductionmentioning

confidence: 99%

Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm

Hag

Handayani

Altalhi

et al. 2021

Sensors

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In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.

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Effective Hybrid Method for the Detection and Rejection of Electrooculogram (EOG) and Power Line Noise Artefacts From Electroencephalogram (EEG) Mixtures

Cited by 7 publications

References 19 publications

Embedded Brain Computer Interface: State-of-the-Art in Research

Embedded Brain Computer Interface: State-of-the-Art in Research

Enhancing EEG-Based Mental Stress State Recognition using an Improved Hybrid Feature Selection Algorithm

Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm

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