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
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 scient...
| Main Authors: | , , , , , , |
|---|---|
| 格式: | 文件 |
| 语言: | English |
| 出版: |
Hindawi Limited
2023-01-01
|
| 丛编: | International Journal of Telemedicine and Applications |
| 在线阅读: | http://dx.doi.org/10.1155/2023/7741735 |
| _version_ | 1827000300676841472 |
|---|---|
| author | A. S. Albahri Z. T. Al-qaysi Laith Alzubaidi Alhamzah Alnoor O. S. Albahri A. H. Alamoodi Anizah Abu Bakar |
| author_facet | A. S. Albahri Z. T. Al-qaysi Laith Alzubaidi Alhamzah Alnoor O. S. Albahri A. H. Alamoodi Anizah Abu Bakar |
| author_sort | A. S. Albahri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T13:58:10Z |
| format | Article |
| id | doaj.art-c067b27d95044a77bb57284bc54673ff |
| institution | Directory Open Access Journal |
| issn | 1687-6423 |
| language | English |
| last_indexed | 2025-02-18T10:43:06Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | International Journal of Telemedicine and Applications |
| spelling | doaj.art-c067b27d95044a77bb57284bc54673ff2024-11-02T05:28:01ZengHindawi LimitedInternational Journal of Telemedicine and Applications1687-64232023-01-01202310.1155/2023/7741735A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust MethodologyA. S. Albahri0Z. T. Al-qaysi1Laith Alzubaidi2Alhamzah Alnoor3O. S. Albahri4A. H. Alamoodi5Anizah Abu Bakar6Iraqi Commission for Computers and Informatics (ICCI)Department of Computer ScienceSchool of MechanicalSouthern Technical UniversityComputer Techniques Engineering DepartmentFaculty of Computing and Meta-Technology (FKMT)School of Computer ScienceThe 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.http://dx.doi.org/10.1155/2023/7741735 |
| spellingShingle | A. S. Albahri Z. T. Al-qaysi Laith Alzubaidi Alhamzah Alnoor O. S. Albahri A. H. Alamoodi Anizah Abu Bakar 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 International Journal of Telemedicine and Applications |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | http://dx.doi.org/10.1155/2023/7741735 |
| work_keys_str_mv | AT asalbahri asystematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT ztalqaysi asystematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT laithalzubaidi asystematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT alhamzahalnoor asystematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT osalbahri asystematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT ahalamoodi asystematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT anizahabubakar asystematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT asalbahri systematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT ztalqaysi systematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT laithalzubaidi systematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT alhamzahalnoor systematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT osalbahri systematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT ahalamoodi systematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology AT anizahabubakar systematicreviewofusingdeeplearningtechnologyinthesteadystatevisuallyevokedpotentialbasedbraincomputerinterfaceapplicationscurrenttrendsandfuturetrustmethodology |