A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks

Attia, Mohamed; Hettiarachchi, Imali; Hossny, Mohammed; Nahavandi, Saeid

doi:10.1109/isbi.2018.8363685

Cited by 29 publications

(29 citation statements)

References 10 publications

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“…With regard to the mental workload (MW) classification, several studies have been carried out (Yin and Zhang, 2017;Yang et al, 2019;Yin et al, 2019). An adaptive Stacked Denoising Auto Encoder (SDAE) was developed in Attia et al (2018) to tackle cross-session MW classification from EEG, and it was reported that the proposed classifier achieved an accuracy of 95.5%.…”

Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

“…Apart from the aforesaid standalone DL models, researchers have attempted to hybridize different DL models in EEG-based BCI investigations (Narejo et al, 2016;Attia et al, 2018;Yang J. et al, 2018;Dai et al, 2019;Kanjo et al, 2019), with encouraging classification accuracies. In Narejo et al (2016), the authors developed a system for predicting eye state from EEG signals using a hybrid DL architecture consisting of DBN and SAE.…”

Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

“…The accuracy of this hybrid model was reported to be as high as 98.9%. Another hybrid DL architecture based on CNN-RNN was proposed in Attia et al (2018) to classify SSVEP signals in the time domain directly, and it achieved an accuracy of 93.59%. Kanjo et al (2019) proposed a hybrid approach that applied CNN and LSTM-RNN on the raw sensor data.…”

Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

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

et al. 2020

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“…The RNN framework has also been applied to other EEG-based tasks, such as identification of individuals [26], hand motion identification [131], sleep staging [132], and emotion recognition [133]. Attia et al [134] presented a hybrid architecture of a CNN and RNN model to categorise SSVEP signals in the time domain. In the research of applying an RNN to auditory stimulus classification, Moinnereau et al [135] used a regulated RNN reservoir to classify three English vowels: a, u and i.…”

Section: Rnns and Lstmmentioning

confidence: 99%

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Cao

Jolfaei

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

214

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Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

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“…The most familiar model of deep learning is CNN which gets specialized in spatial information exploration [14]. This session has been illustrated the CNN working mechanism briefly whereas it is frequently utilized for identifying the inherent spatial data in application namely image recognition, ubiquitous, and object searching because of its salient features presented are invariance of translation, good local spatial and regulation of structure.…”

Section: A Convolution Neural Network (Cnn)mentioning

confidence: 99%

Brain Computing Interface using Deep Learning for Blind People

Chakravarthy*,

Seenivasan

2019

IJRTE

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The developing area of research in Brain Computer Interface (BCI) is used to enhance the quality of human computer applications. It can be decoding individuals by the computer device signals converted into commands between human’s neural world and outer physical world. The brain use bodies under some circumstances to interact with the external world and also brain can be depressed of their sensing abilities namely blindness or deafness. In this study, analyze of brain’s behavior using BCI for blind people in spatial activity. The common beliefs in blind people using other senses by compensate their lack of vision. In case of BCI system can able to understand the brain’s activity even in very difficult challenge. Therefore we propose the data mining technique. In this research work, deep learning approach based on the framework of Convolution Neural Networks (CNN) with Long Short-Term Memory (LSTM) can help us to discover their brain’s activity for blind people.

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A time domain classification of steady-state visual evoked potentials using deep recurrent-convolutional neural networks

Cited by 29 publications

References 10 publications

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

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

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Brain Computing Interface using Deep Learning for Blind People

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