A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computer Science

Background:

• Motor imagery (MI) brain-computer interfaces (BCIs) leverage neural activity from imagined movements for communication.
• Significant annual research advances occur in MI-BCI, yet practical application remains limited.
• Electroencephalogram (EEG) is a primary modality for MI-BCI due to its non-invasiveness.

Purpose of the Study:

• To comprehensively review the current state of EEG-based MI-BCI systems.
• To analyze the entire MI-BCI pipeline, from data acquisition to classification.
• To identify recent developments and critical challenges for commercial deployment.

Main Methods:

• Systematic literature review of EEG-based MI-BCI research.
• Analysis of MI-BCI pipeline stages: data acquisition, MI training, preprocessing, feature extraction, channel/feature selection, and classification.
• Discussion of algorithmic issues and recent advancements relevant to commercialization.

Main Results:

• Detailed overview of state-of-the-art techniques in each MI-BCI pipeline stage.
• Identification of key challenges hindering the transition from lab environments to real-world applications.
• Highlighting of recent algorithmic improvements and their potential impact on commercial viability.

Conclusions:

• EEG-based MI-BCI technology has advanced significantly but remains largely confined to laboratory settings.
• Addressing critical algorithmic issues and recent developments is crucial for successful commercial deployment.
• Further research is needed to bridge the gap between current capabilities and practical, widespread use.