Combination of EEG Data Time and Frequency Representations in Deep Networks for Sleep Stage Classification

Manzano, Martí; Guillén, Alberto; Rojas, Ignacio; Herrera, José Benavente

doi:10.1007/978-3-319-63312-1_20

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Tan et al [54] adopted a DBN-RBM algorithm to detect sleep spindle based on power spectral density (PSD) features extracted from sleep EEG signals and achieved an F-1 of 92.78% (iii) Hybrid models. Manzano et al [55] presented a multi-view algorithm in order to predict sleep stage by combining CNN and MLP. The CNN was employed to receive the raw time-domain EEG oscillations while the MLP received the spectrum singles processed by the short-time Fourier transform among 0.5-32 Hz.…”

Section: Spontaneous Eegmentioning

confidence: 99%

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

et al. 2021

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Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

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Section: Spontaneous Eegmentioning

confidence: 99%

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

et al. 2021

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“…There are some rules of thumb for image classification, but, since the problem tackled in this paper belongs to another domain, those rules could not be adequate. To overcome this problem, the hyperparameter space has been clustered based on the experience tackling similar problems like [23,24], so that a finite number of values are evaluated. Finally, after evaluating different topologies with the validation data set, the fittest one was composed of three convolutional layers which were enough to cover the inner complexity of the data and extract relevant features from the raw data.…”

Section: Models Using Convolutional Neural Networkmentioning

confidence: 99%

Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning

González¹,

Conceição²,

Pimenta³

et al. 2021

Preprint

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This paper presents several approaches to deal with the problem of identifying muons in a water Cherenkov detector with a reduced water volume and 4 PMTs. Different perspectives of information representation are used and new features are engineered using the specific domain knowledge. As results show, these new features, in combination with the convolutional layers, are able to achieve a good performance avoiding overfitting and being able to generalise properly for the test set. The results also prove that the combination of state-of-the-art Machine Learning analysis techniques and water Cherenkov detectors with low water depth can be used to efficiently identify muons, which may lead to huge investment savings due to the reduction of the amount of water needed at high altitudes. This achievement can be used in further research to be able to discriminate between gamma and hadron induced showers using muons as discriminant.

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“…(iii) Hybrid models. Manzano et al [128,129] proposed a multi-view model to predict sleep stage by combining CNN and MLP. e CNN was employed to receive the raw EEG data in the time domain while the MLP received the spectrum obtained by a Short-Time Fourier Transform (STFT) between 0.5-32 Hz.…”

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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Combination of EEG Data Time and Frequency Representations in Deep Networks for Sleep Stage Classification

Cited by 7 publications

References 17 publications

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning

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

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