Convolutional neural networks for sleep stage scoring on a two-channel EEG signal

Fernández-Blanco, Enrique; Rivero, Daniel

doi:10.1007/s00500-019-04174-1

Cited by 34 publications

(27 citation statements)

References 51 publications

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“…Having a large number of EEG channels makes the whole data analysis process more complex, which can be eased by inter alia selection of only those necessary channels [ 37 , 38 , 39 , 40 ]. It also allows reducing the set up time [ 37 , 41 ]. Moreover, using less channels enables to make the BCI systems more compact [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…Very limited number of channels (only two) is applied in inter alia polysomnography studies, where EEG data is one of the analysed. So far, such an amount of electrodes seems to be enough for research or medical purposes [ 41 ]. The use of six channels only in BCI applications can be found in numerous studies and it has been proven to provide similar to the expanded channel sets performance [ 42 , 43 , 44 , 45 , 46 ].…”

Section: Methodsmentioning

confidence: 99%

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Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

et al. 2021

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Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky–Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

et al. 2021

View full text Add to dashboard Cite

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“…Model performance was evaluated by accuracy, sensitivity, precision, Cohen's kappa and F1 score. The detailed definition can be found in previous studies [7], [26], [27].…”

Section: Performance Assessmentmentioning

confidence: 99%

A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series

Yan

Zhou

et al. 2021

2020 28th European Signal Processing Conference (EUSIPCO)

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“…In this paper, the authors proposed a convolutional network architecture based on three dimensions of the incoming image. Moreover, CNN was used for classifying objects from different points of view, which is very practical using drones [5], or even for sleep stage scoring based on electroencephalogram (EEG) signals [6]. Moreover, CNN has been used for recognition of vehicle driving behavior [7].…”

Section: Related Workmentioning

confidence: 99%

Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism

Połap¹,

Wlodarczyk–Sielicka

2020

Sensors

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The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of non-conventional vessels are not widely known. These methods, based on image samples, can be considered difficult. This paper is intended to show an alternative way to approach image classification problems; not by classifying the entire input data, but smaller parts. The described solution is based on splitting the image of a ship into smaller parts and classifying them into vectors that can be identified as features using a convolutional neural network (CNN). This idea is a representation of a bag-of-words mechanism, where created feature vectors might be called words, and by using them a solution can assign images a specific class. As part of the experiment, the authors performed two tests. In the first, two classes were analyzed and the results obtained show great potential for application. In the second, the authors used much larger sets of images belonging to five vessel types. The proposed method indeed improved the results of classic approaches by 5%. The paper shows an alternative approach for the classification of non-conventional vessels to increase accuracy.

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Convolutional neural networks for sleep stage scoring on a two-channel EEG signal

Cited by 34 publications

References 51 publications

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation

A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series

Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism

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