Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals

Wen, Tingxi; Zhang, Zhongnan

doi:10.1109/access.2018.2833746

Cited by 165 publications

(71 citation statements)

References 25 publications

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“…In the market there are different options that allow EEG portable reading and at low cost, compared to other more sophisticated medical options, among which the EEG diadems stand out [19]. It is possible to completely characterize the MindWave Mobile headset signals, as developers this information is very useful, and it gives a broad overview of the engineering applications that can be given for different projects, Figure 7 show the basics elements of this device [20], [21].…”

Section: Fig 6 Eeg Anatomymentioning

confidence: 99%

Help Tetraplegic People by Means of a Computational Neuronal Control System

Moreno¹,

Matamoros²,

Tejeida

et al. 2019

ijacsa

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In the present document we present an Interface called BrainMouse where its main task is to help people with motor disabilities specially tetraplegic or quadriplegic people that they can move the mouse of the computer by means of blinking or any neural response. The Interface uses the data obtained from a neuronal system which is responsible for taking reliable readings of the electrical signals generated in the human brain, through non-intrusive neuronal interfaces. The recorded data is used by the BrainMouse Interface so that the mouse can perform functions such as an up, down, left lateral, right lateral, left click, right click and double click. Thus, this interface has all the options that a conventional mouse would have.

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Section: Fig 6 Eeg Anatomymentioning

confidence: 99%

Help Tetraplegic People by Means of a Computational Neuronal Control System

Moreno¹,

Matamoros²,

Tejeida

et al. 2019

ijacsa

View full text Add to dashboard Cite

show abstract

“…For instance, Acharya et al explored seven di erent machine learning algorithms to investigate the automated diagnosis of epileptic EEG using entropies [23]. Recently, some scholars even tried to employ deep learning to analyze physiological signals [24].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Wang

et al. 2020

Complexity

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Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.

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“…Such a reduction enables to distinguish signals representing different types of mental activity that the BCI system is to recognize [10,30]. However, in deep learning classification, feature extraction is not always applied as signal characteristics may be automatically derived from autoencoders [31,32]. Moreover, Wu et al proposed an experimental scenario in which the feature selection and classification were performed simultaneously [33].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Browarczyk

Kurowski

Kostek

2020

Sensors

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The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch's method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

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Deep Convolution Neural Network and Autoencoders-Based Unsupervised Feature Learning of EEG Signals

Cited by 165 publications

References 25 publications

Help Tetraplegic People by Means of a Computational Neuronal Control System

Help Tetraplegic People by Means of a Computational Neuronal Control System

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

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