PMID- 37168809
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230514
IS  - 1687-6415 (Print)
IS  - 1687-6423 (Electronic)
IS  - 1687-6415 (Linking)
VI  - 2023
DP  - 2023
TI  - 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.
PG  - 7741735
LID - 10.1155/2023/7741735 [doi]
LID - 7741735
AB  - 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.
CI  - Copyright (c) 2023 A. S. Albahri et al.
FAU - Albahri, A S
AU  - Albahri AS
AUID- ORCID: 0000-0003-3335-457X
AD  - Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq.
FAU - Al-Qaysi, Z T
AU  - Al-Qaysi ZT
AD  - Department of Computer Science, Computer Science and Mathematics College, Tikrit 
      University, Tikrit, Iraq.
FAU - Alzubaidi, Laith
AU  - Alzubaidi L
AUID- ORCID: 0000-0002-7296-5413
AD  - School of Mechanical, Medical, and Process Engineering, Queensland University of 
      Technology, Brisbane, QLD 4000, Australia.
AD  - ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland 
      University of Technology, Brisbane, QLD 4000, Australia.
FAU - Alnoor, Alhamzah
AU  - Alnoor A
AUID- ORCID: 0000-0003-2873-2054
AD  - Southern Technical University, Basrah, Iraq.
FAU - Albahri, O S
AU  - Albahri OS
AUID- ORCID: 0000-0002-7844-3990
AD  - Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, 
      Iraq.
AD  - Department of Computer Science and Information Technology, La Trobe University, 
      Melbourne, VIC, Australia.
FAU - Alamoodi, A H
AU  - Alamoodi AH
AUID- ORCID: 0000-0003-4393-5570
AD  - Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan 
      Idris (UPSI), Perak, Malaysia.
FAU - Bakar, Anizah Abu
AU  - Bakar AA
AUID- ORCID: 0000-0002-4030-053X
AD  - School of Computer Science, Universiti Sains Malaysia, Malaysia.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20230430
PL  - Egypt
TA  - Int J Telemed Appl
JT  - International journal of telemedicine and applications
JID - 101467196
PMC - PMC10164869
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2023/05/12 01:07
MHDA- 2023/05/12 01:08
PMCR- 2023/04/30
CRDT- 2023/05/11 19:27
PHST- 2022/09/25 00:00 [received]
PHST- 2023/02/01 00:00 [revised]
PHST- 2023/03/16 00:00 [accepted]
PHST- 2023/05/12 01:08 [medline]
PHST- 2023/05/12 01:07 [pubmed]
PHST- 2023/05/11 19:27 [entrez]
PHST- 2023/04/30 00:00 [pmc-release]
AID - 10.1155/2023/7741735 [doi]
PST - epublish
SO  - Int J Telemed Appl. 2023 Apr 30;2023:7741735. doi: 10.1155/2023/7741735. 
      eCollection 2023.