A deep learning-based multi-model ensemble method for eye state recognition from EEG

Islalm, Shafiqul; Rahman, M. M. Hafizur; Rahman, Md. Hafizur; Hoque, Robiul; Roonizi, Arman Kheirati; Aktaruzzaman, Md.

doi:10.1109/ccwc51732.2021.9376084

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…[winner, loser] = compete x besi , x t+1 (10) Generally, a probabilistic method for compact BA was applied for representing the bat solution set in which the position or velocity is saved, but a recently created candidate is saved. A variable ω is utilized as a weight for controlling the likelihood of μ i sampling in PDF among left [−1, μ i ] i.e., P L (x) , for-l ≤ x ≤ μ j , and right [1] i.e., P R (x) , for μ i ≤ x ≤ 1.…”

Section: Parameter Tuning Using Cbamentioning

confidence: 99%

“…In CNN feature techniques were effectual became concerned issue dependent and subject independent eye state EEG classifiers. Islam et al [10] presented 3 frameworks of DL technique utilizing ensemble approach to eye state detection (open/close) in EEG directly. The analysis was implemented on freely accessible publicly EEG eye state data set of 14980 instances.…”

Section: Introductionmentioning

confidence: 99%

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Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Larabi-Marie-Sainte¹,

Alabdulkreem²,

Alamgeer³

et al. 2022

Computers, Materials &Amp; Continua

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Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature vectors. In addition, extreme learning machine autoencoder (ELM-AE) model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using CBA. The experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.

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Section: Parameter Tuning Using Cbamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Larabi-Marie-Sainte¹,

Alabdulkreem²,

Alamgeer³

et al. 2022

Computers, Materials &Amp; Continua

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“…This method got an accuracy of about 85%. For eye state recognition (open or closed) from electroencephalography (EEG), the authors in [6] proposed three architectures: convolution neural network (CNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The proposed method achieved an accuracy rate of 99.86%.…”

Section: The Physiological Features-based Techniquesmentioning

confidence: 99%

“…Deep learning has recently attracted a lot of research. It has rapidly developed and succeeded in many domains, including speech recognition, sensor data, motion graphics, spectrographs, ECG, electronics devices, and simulated data [5,6]. For example, convolutional neural networks (CNNs) in detecting drowsy drivers have been widely studied.…”

Section: Introductionmentioning

confidence: 99%

Modern Drowsiness Detection in Deep Learning: A review

Jasim,

Abdul Hassan

2022

J. Al-Qadisiyah Comp. Sci. Math.

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Recent statistics show that drowsiness is now more of a factor in some car accidents than alcohol. So numerous monitoring systems have been developed to reduce, and accidents eliminate these events. Drowsiness detection systems are plentiful, but it isn't clear which one is the most effective. This review paper will address the following issues: firstly, determining the extracted features, whether behavioral-based, physiological-based, or vehicle-based, which are used to detect driver drowsiness; secondly, focusing on using deep learning to detect driver drowsiness. Finally, concluding from this study that the hybrid-based features should be used Because it gives strength in determining the drowsiness of the driver.

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“…On the other hand, in the EEG technique [16,17], the electrical signals produced by the brain are measured using electrodes placed on the scalp of the user. The computational complexity associated with the algorithms employed in the image-based methods, such as VOG, is considerably higher than those used in EOG and EEG due to the costly process of analyzing and classifying multiple images [18]. The EOG method seems to be an interesting technique for building HMIs based on eye movements or blinking, but the placement of electrodes on the user's face might be uncomfortable and not usable in practical applications [19].…”

Section: Introductionmentioning

confidence: 99%

Eye State Identification Based on Discrete Wavelet Transforms

et al. 2021

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We present a prototype to identify eye states from electroencephalography signals captured from one or two channels. The hardware is based on the integration of low-cost components, while the signal processing algorithms combine discrete wavelet transform and linear discriminant analysis. We consider different parameters: nine different wavelets and two features extraction strategies. A set of experiments performed in real scenarios allows to compare the performance in order to determine a configuration with high accuracy and short response delay.

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A deep learning-based multi-model ensemble method for eye state recognition from EEG

Cited by 7 publications

References 18 publications

Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Modern Drowsiness Detection in Deep Learning: A review

Eye State Identification Based on Discrete Wavelet Transforms

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