Online Eye state recognition from EEG data using Deep architectures

Reddy, Tharun Kumar; Behera, Laxmidhar

doi:10.1109/smc.2016.7844325

Cited by 30 publications

(8 citation statements)

References 13 publications

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“…The recurrent quantum neural network filtering technique is implemented in BCI system with a goal of filtering EEG signals before attributes detection and identification to increase the classification outcome [26]. The multilayer perceptron neural networks (MLP) with stochastic gradient descent algorithm was utilize in [27] to recognize the eye state. Researchers in [28] proposed various algorithms to improve the convergence speed and classification accuracy with neural networks, while many deep learning based approaches have also been suggested in BCI with driver drowsiness detection applications [29].…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

et al. 2019

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The robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet transform (MEWT) algorithm for the decoding of different MI tasks. The main contributions of this study are four-fold. First, the multiscale principal component analysis method is utilized in the preprocessing module to obtain robustness against noise. Second, a novel automated channel selection strategy is proposed and then is further verified with comprehensive comparisons among three different strategies for decoding channel combination selection. Third, a sub-band alignment method by utilizing MEWT is adopted to obtain joint instantaneous amplitude and frequency components for the first time in MI applications. Four, a robust correlation-based feature selection strategy is applied to largely reduce the system complexity and computational load. Extensive experiments for subject-specific and subject independent cases are conducted with the three-benchmark datasets from BCI competition III to evaluate the performances of the proposed method by employing typical machine-learning classifiers. For subject-specific case, experimental results show that an average sensitivity, specificity and classification accuracy of 98% was achieved by employing multilayer perceptron neural networks, logistic model tree and least-square support vector machine (LS-SVM) classifiers, respectively for three datasets, resulting in an improvement of upto 23.50% in classification accuracy as compared with other existing method. While an average sensitivity, specificity and classification accuracy of 93%, 92.1% and 91.4% was achieved for subject independent case by employing LS-SVM classifier for all datasets with an increase of up to 18.14% relative to other existing methods. Results also show that our proposed algorithm provides a classification accuracy of 100% for subjects with small training size in subject-specific case, and for subject independent case by employing a single source subject. Such satisfactory results demonstrate the great potential of the proposed MEWT algorithm for practical MI EEG signals classification.

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Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

et al. 2019

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“…Also, the present study is a first step towards realizing large scale Multi-task learning BCIs. The two most prominent pre-training approaches for DNN's are the RBM [45] and stacked auto-encoder [46] algorithms. But, both of the above algorithms are unsupervised.…”

Section: Discussionmentioning

confidence: 99%

Electroencephalogram Based Reaction Time Prediction With Differential Phase Synchrony Representations Using Co-Operative Multi-Task Deep Neural Networks

Reddy

Arora

Kumar

et al. 2019

IEEE Trans. Emerg. Top. Comput. Intell.

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Driver drowsiness is receiving a lot of deliberation as it is a major cause of traffic accidents. This paper proposes a method which utilizes the fuzzy common spatial pattern optimized differential phase synchrony representations to inspect electroencephalogram (EEG) synchronization changes from the alert state to the drowsy state. EEG-based reaction time prediction and drowsiness detection are formulated as primary and ancillary problems in the context of multi-task learning. Statistical analysis results suggest that our method can be used to distinguish between alert and drowsy state of mind. The proposed Multi-Task DeepNet (MTDNN) performs superior to the baseline regression schemes, like support vector regression (SVR), least absolute shrinkage and selection operator, ridge regression, K-nearest neighbors, and adaptive neuro fuzzy inference scheme (ANFIS), in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) metrics. In particular, the best performing multi-task network MTDNN 5 recorded a 15.49% smaller RMSE, a 27.15% smaller MAPE, and a 10.13% larger CC value than SVR.

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“…ey tried a DBN-RBM with three RBMs and a DBN-AE with three AEs and achieved a very high accuracy of 98.9%. Reddy et al [159] tried a simpler structure, MLP, for eye state detection and got a slightly lower accuracy of 97.5%.…”

Section: Eeg Oscillatorymentioning

confidence: 99%

A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

Zhang

Yao

Wang

et al. 2019

Preprint

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Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has enhanced the performance of brain-computer interface systems signi cantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions, most published in the past ve years. Finally, we discuss the applied areas, emerging challenges, and future directions for deep learning-based BCI.

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Online Eye state recognition from EEG data using Deep architectures

Cited by 30 publications

References 13 publications

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

Electroencephalogram Based Reaction Time Prediction With Differential Phase Synchrony Representations Using Co-Operative Multi-Task Deep Neural Networks

A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

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