Albahri A S, Al-Qaysi Z T, Alzubaidi Laith, Alnoor Alhamzah, Albahri O S, Alamoodi A H, Bakar Anizah Abu
Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq.
Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq.
Int J Telemed Appl. 2023 Apr 30;2023:7741735. doi: 10.1155/2023/7741735. eCollection 2023.
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% ( = 21/30). The second category, recurrent neural network (RNN), accounts for 10% ( = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% ( = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% ( = 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.
通过系统综述评估深度学习技术在基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）应用中的意义。考虑了三个可靠的数据库，即PubMed、ScienceDirect和IEEE，以收集相关的科学和理论文章。最初，在2010年至2021年期间发现了125篇与该综合研究领域相关的论文。经过筛选过程，仅确定了30篇文章，并根据其深度学习方法的类型分为五类。第一类，卷积神经网络（CNN），占70%（=21/30）。第二类，循环神经网络（RNN），占10%（=3/30）。第三类和第四类，深度神经网络（DNN）和长短期记忆（LSTM），各占6%（=3/30）。第五类，受限玻尔兹曼机（RBM），占3%（=1/30）。研究了文献中关于深度学习模式识别技术在基于SSVEP的BCI现有应用中确定的主要方面的研究结果，如特征提取、分类、激活函数、验证方法和实现的分类准确率。还进行了全面的映射分析，确定了六个类别。讨论了在基于SSVEP的BCI应用中确保可信深度学习的当前挑战，并向研究人员和开发人员提供了建议。该研究批判性地回顾了基于SSVEP的BCI应用在基于深度学习技术的开发挑战和基于多准则决策（MCDM）的选择挑战方面当前未解决的问题。提出了一个信任提议解决方案，包括三个方法阶段，用于使用模糊决策技术评估和基准测试基于SSVEP的BCI应用。为基于SSVEP的BCI和深度学习领域的研究人员和开发人员提供了有价值的见解和建议。
