A review of user training methods in brain computer interfaces based on mental tasks

Area of Science:

• Neuroscience
• Human-Computer Interaction
• Rehabilitation Engineering

Background:

• Mental-tasks based brain-computer interfaces (MT-BCIs) offer promising assistive technology but face reliability challenges outside labs.
• Effective user training is crucial for MT-BCI control, yet the learning process and optimal training strategies remain under-investigated.
• Current MT-BCI training paradigms are often sub-optimal, necessitating improvements for wider adoption.

Purpose of the Study:

• To provide a comprehensive overview of current MT-BCI user training methodologies.
• To categorize and taxonomize existing training approaches based on key components like environment, instructions, feedback, and exercises.
• To offer guidelines for selecting optimal training methods and identify future research directions.

Main Methods:

• Systematic review of existing literature on MT-BCI user training.
• Development of a categorization and taxonomy for different training approaches.
• Analysis of human factors influencing MT-BCI learning and performance.

Main Results:

• Identified and categorized various training methods, including environmental adaptations, instructional strategies, feedback mechanisms, and exercise designs.
• Highlighted the importance of an interdisciplinary approach and adaptive training tailored to individual user needs.
• Established a framework for understanding and comparing different MT-BCI training strategies.

Conclusions:

• Optimizing MT-BCI user training requires considering human factors and adopting interdisciplinary, adaptive approaches.
• The proposed taxonomy and guidelines can aid researchers and developers in selecting and designing more effective training programs.
• Further research is needed to address open challenges and enhance the reliability and usability of MT-BCIs.