Status of deep learning for EEG-based brain-computer interface applications.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computer Science

Background:

• Brain-computer interface (BCI) technology has advanced significantly due to breakthroughs in central nervous system bioinformatics and computational innovation.
• BCI applications are crucial for neurorehabilitation in patients with physical disabilities and brain injuries, such as stroke.
• Historically, electroencephalogram (EEG)-based BCI methods relied on matrix factorization and machine learning due to limited data.

Purpose of the Study:

• To review deep learning-based approaches for EEG-based BCI applications published between 2017 and 2022.
• To analyze the merits, drawbacks, and applications of these deep learning models.
• To identify current challenges and future research directions in the field.

Main Methods:

• Systematic review of recent literature on deep learning models for EEG-based BCI.
• Analysis of studies focusing on motor imagery classification, epileptic seizure detection, and driver attention recognition.
• Comparative evaluation of different deep learning architectures and their performance.

Main Results:

• The availability of large, high-quality EEG datasets has spurred the adoption of deep learning in BCI research.
• Deep learning models show great promise for complex EEG data analysis tasks.
• Significant progress has been made in various EEG-based BCI applications using deep learning.

Conclusions:

• Deep learning is a rapidly growing area in EEG-based BCI research, offering powerful tools for analysis and application.
• Further research is needed to address current challenges and fully exploit the potential of deep learning in BCI.
• This review provides valuable insights for researchers in the EEG-based BCI community.