A comprehensive review of EEG-based brain–computer interface paradigms

By Reza Abiri, Soheil Borhani, Eric W. Sellers, Yang Jiang, Xiaopeng Zhao. Journal of Neural Engineering, 2019

848 citations recorded by the source corpus.

Advances in brain science and computer technology in the past decade have led to exciting developments in brain–computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/be85690a6714fe241536a00f436f671bbddd4281

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In Cognitive Neuroscience

Citations in the corpus

A Survey on the Metaverse: The State-of-the-Art, Technologies, Applications, and Challenges (2023, background): “Abiri et al. [51] reviewed the electroencephalography (EEG)- based BCI approach.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “However, a comparison of the experimental results between the BCI applications or EEG modalities were not made available, and a similar observation was also noticed in Abiri et al. (2019).”
Methods for Improving Brain-Computer Interface: Using A Brain-Directed Adjuvant and A Second-Generation Artificial Intelligence System to Enhance Information Streaming and Effectiveness of Stimuli (2023, unknown): “Wireless recording, machine learning analysis, and real-time temporal resolution can improve EEG-based BCI [11].”
Macroscopic EEG reveals discriminative low-frequency oscillations in plan-to-grasp visuomotor tasks (2025, unknown): “…planning and motor processes involved during grasp execution could be advantageous, as it may introduce a new naturalistic control strategy for BMI systems, as patients with severe motor impairments require systems that bypass impaired motor pathways and instead decode cognitive signals [8, 46].”