Deep learning for EEG data analytics: A survey

Li, Gen; Lee, Chang Ha; Jung, Jason J.; Youn, Young Chul; Camacho, David

doi:10.1002/cpe.5199

Cited by 65 publications

(34 citation statements)

References 68 publications

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“…Regarding the choice of the optimization algorithm, many optimizers exist in the literature such as Adam [ 105 ], Stochastic Gradient Descent Optimizer (SGD) [ 106 ] and Root Mean Square Propagation (RMS prop) [ 107 ]. In this context, SGD is the most popular optimizer, which is simple and effective for finding optimal values in a neural network.…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…These logical sequences are rich in content and possess a complex time relationship with each other. The key concept of RNN is that the hidden state of the current network will retain the previous input information, and it is used for the next current network (Li et al, 2019). There are two typical RNN architectures that have attracted much attention and achieved great success: long short-term memory (LSTM) and gated recurrent units (GRU).…”

Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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“…Its applica-13 tion ranges from; clinical capacity such as sleep disorder studies, seizure detection to 14 commercial circumstances such as EEG-controlled games [1].The EEG data is a matrix 15 consisting of electric potentials. This form of EEG data makes it easy to use machine 16 learning models [2]. With its high temporal resolution, EEG data can provide information 17 regarding the functional connectivity within the brain, thereby providing a topolog-18 ical understanding of the functioning of the human brain [3].…”

Section: Introduction 12mentioning

confidence: 99%

Application of Graph Theory Features towards EEG Data Classification Models for Working Memory and The Emotional States

Prakash¹,

G²,

Baths³

2021

Preprint

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Functional Connectivity analysis using Electroencephalography signals is a common 2 practice. The EEG signals are converted to networks by transforming the signals into a correlation 3 matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regres 4 sion, Random Forest, Support Vector Machine, and Recurrent Neural Networks, are implemented 5 on the correlation matrix data to classify them either on their psychometric assessment or the 6 effect of therapy. The classifications based on RNN provided higher accuracy( 74-88%) compared 7 to the other three models( 50-78%). The use of a correlation matrix, instead of using individual 8 graph features provides an initial test of the data. When compared with the time-resolved correlation 9 matrix it provided 4-5% higher accuracy.

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Deep learning for EEG data analytics: A survey

Cited by 65 publications

References 68 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Application of Graph Theory Features towards EEG Data Classification Models for Working Memory and The Emotional States

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