Classification of mental tasks in the prefrontal cortex using fNIRS.

Area of Science:

• Neuroscience
• Brain-Computer-Interface (BCI) research

Background:

• Functional near infrared spectroscopy (fNIRS) is increasingly utilized in neuroscience and BCI development.
• Current single-trial fNIRS analysis predominantly focuses on motor imagery and mental arithmetic tasks.
• A need exists to evaluate diverse mental tasks for their efficacy in fNIRS-based BCIs.

Purpose of the Study:

• To investigate the suitability of different mental tasks for fNIRS-based Brain-Computer Interfaces (BCIs).
• To systematically compare classification accuracies for mental arithmetics, word generation, and mental rotation tasks.
• To provide empirical data on the performance of various cognitive tasks within an fNIRS-BCI framework.

Main Methods:

• Data acquisition from 10 subjects performing three distinct mental tasks: mental arithmetics, word generation, and mental rotation.
• Utilized functional near infrared spectroscopy (fNIRS) to measure brain activity.
• Performed single-trial analysis and classification accuracy assessment for each task.

Main Results:

• The study presents the first systematic comparison of classification accuracies for these mental tasks in fNIRS-BCIs.
• Significant differences in classification accuracies were observed among the evaluated mental tasks.
• Results indicate varying degrees of suitability for different cognitive tasks in fNIRS-BCI applications.

Conclusions:

• The choice of mental task significantly impacts classification accuracy in fNIRS-based BCIs.
• Mental arithmetics, word generation, and mental rotation exhibit differential performance.
• Further research is warranted to optimize task selection for enhanced fNIRS-BCI performance.