Brain-Computer Interface based User Authentication System for Personal Device Security

Hossain, Tabassum; Rakshit, Arnab; Konar, Amit

doi:10.1109/iccece48148.2020.9223069

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Additionally, the CCA is used with LSTM in [ 45 ] for classifying multiflicker-SSVEP in single-channel dry-EEG for low-power/high-accuracy quadcopter-BMI systems. In the study [ 27 ], the CCA with the RRN classifier is used in an SSVEP-based BCI system for user authentication in a personal device. The CCA is also used with EEGNet and ensemble learning to improve the cross-session classification of SSVEP-based BCI from Ear-EEG.…”

Section: Systematic Results and Discussionmentioning

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%

“…In the context of SSVEP, CNNs are structured with five layers: an input layer, a convolutional layer, a linear unit layer, a pooling layer, and a fully connected layer [ 14 , 15 ]. These layers are organized into three dimensions: height, width, and depth [ 27 ] (see Figure 2 ).…”

Section: Introductionmentioning

confidence: 99%

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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“…These systems allow the control of orthoses, prostheses, or FES devices to assist disabled patients during therapy ( Stan et al, 2015 ; Zhao et al, 2016 ). The most common application of these BCI systems is for spellers (at least 30% of papers), but for the device control there are wheelchairs ( Zhang et al, 2014 , 2016 ; Turnip et al, 2015 ; Lopes et al, 2016 ; Waytowich and Krusienski, 2017 ; Yu et al, 2017 ; Chen et al, 2020 ), robots ( Zhao et al, 2015 ; Çiğ et al, 2017 ; Venuto et al, 2017 ; Erkan and Akbaba, 2018 ; Yuan et al, 2018 ; Khadijah et al, 2019 ; Wang et al, 2020 ), and domotics tools ( Venuto and Mezzina, 2018 ; Hossain et al, 2020 ; Lee T. et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

Gutiérrez-Martínez

Mercado-Gutierrez

Carvajal-Gámez

et al. 2021

Front. Hum. Neurosci.

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Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

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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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Brain-Computer Interface based User Authentication System for Personal Device Security

Cited by 7 publications

References 13 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

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

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

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