Applying deep-learning to a top-down SSVEP BMI

Ahn, Min-Hee; Min, Byoung-Kyong

doi:10.1109/iww-bci.2018.8311526

Cited by 6 publications

(9 citation statements)

References 8 publications

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“…Many studies have utilized this process in their feature extraction procedures in the current literature. For example, in [ 37 ], deep learning is applied to a top-down control strategy utilizing SSVEP brain signals. At the same time, [ 59 ] employs a convolutional neural network (CNN) for classifying SSVEP in an ambulatory environment.…”

Section: Systematic Results and Discussionmentioning

confidence: 99%

“…Different deep learning techniques should be examined to enhance top-down SSVEP BMI's decoding accuracy [ 37 ]. Optimizing the hyperparameter is one of the important research directions.…”

Section: Recommendations To Researchers and Developersmentioning

confidence: 99%

“…Complex layers, including hidden ones, are more involved in top-down SSVEP-based cognitive BCIs than those with physically driven bottom-up ones, with even the physiological processes of such layers being unknown [ 13 , 31 – 36 ]. This issue can be solved by implementing deep learning, which provides more optimal decoding performance by learning from raw and image data to decode SSVEP brain signals through understanding each SSVEP stimulus feature [ 10 , 36 , 37 ]. The critical analyses of current academic literature reviews for deep learning in SSVEP-based BCI applications are discussed further as it is significant to various neurology and neuroscience research areas.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

Albahri

Al-qaysi

Alzubaidi

et al. 2023

International Journal of Telemedicine and Applications

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The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% ( n = 21 / 30 ). The second category, recurrent neural network (RNN), accounts for 10% ( n = 3 / 30 ). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% ( n = 30 ). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% ( n = 1 / 30 ). The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

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Section: Systematic Results and Discussionmentioning

confidence: 99%

“…Different deep learning techniques should be examined to enhance top-down SSVEP BMI's decoding accuracy [ 37 ]. Optimizing the hyperparameter is one of the important research directions.…”

Section: Recommendations To Researchers and Developersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

Albahri

Al-qaysi

Alzubaidi

et al. 2023

International Journal of Telemedicine and Applications

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“…fMRI has several aws compared to fNIRS: 1) fMRI requires an expensive scanner to generate magnetic elds; 2) the scanner is heavy and has poor portability. Figure 9 6 shows the fMRI acquisition machine, and the resulting brain images. fMRI images of speech perception and nger taping have a signi cant di erence, which indicates that it has high SNR.…”

Section: Functional Magnetic Resonance Imaging (Fmri)mentioning

confidence: 99%

“…(3) SSEP. Most deep-learning based studies in the SSEP eld focus on SSVEP, such as [6,98]. SSVEP are neural oscillations from the parietal and occipital regions of the brain evoked from ickering visual stimuli.…”

Section: Evoked Potentialmentioning

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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A comprehensive review of deep learning power in steady-state visual evoked potentials

Al-Qaysi,

Albahri,

Ahmed

et al. 2024

Neural Comput & Applic

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Applying deep-learning to a top-down SSVEP BMI

Cited by 6 publications

References 8 publications

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

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

A comprehensive review of deep learning power in steady-state visual evoked potentials

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