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A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based

A S Albahri1, Z T Al-Qaysi2, Laith Alzubaidi3,4

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Summary
This summary is machine-generated.

Deep learning significantly enhances steady-state visually evoked potential (SSVEP) brain-computer interfaces (BCIs). This review analyzes deep learning methods, challenges, and proposes trust solutions for SSVEP-BCI applications.

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCIs) translate brain activity into commands.
  • Steady-state visually evoked potentials (SSVEPs) are a common BCI signal.
  • Deep learning (DL) offers advanced pattern recognition for BCIs.

Purpose of the Study:

  • To systematically review DL techniques in SSVEP-BCI applications.
  • To analyze DL methods, challenges, and trust in SSVEP-BCIs.
  • To provide recommendations for researchers and developers.

Main Methods:

  • Systematic literature review of PubMed, ScienceDirect, and IEEE (2010-2021).
  • Filtering 125 papers to 30 relevant articles.
  • Classification of DL methods (CNN, RNN, DNN, LSTM, RBM) and analysis of key aspects.

Main Results:

  • Convolutional Neural Networks (CNNs) dominate SSVEP-BCI research (70%).
  • Key aspects analyzed include feature extraction, classification, and accuracy.
  • Identified challenges in trustworthy DL for SSVEP-BCIs.

Conclusions:

  • DL significantly impacts SSVEP-BCI performance.
  • Addressing trust and development challenges is crucial.
  • A fuzzy decision-making approach is proposed for benchmarking SSVEP-BCIs.