Reduction of Delay in Detecting Initial Dips from Functional Near-Infrared Spectroscopy Signals Using Vector-Based...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Detecting initial dips in hemodynamic responses is crucial for understanding brain activity.
• Existing methods often suffer from significant time lags, limiting real-time applications.
• Functional near-infrared spectroscopy (fNIRS) offers a non-invasive way to monitor brain hemodynamics.

Purpose of the Study:

• To develop and validate a systematic method for reducing the time lag in detecting initial dips.
• To improve the speed and accuracy of initial dip detection for real-time brain-computer interfacing (BCI).
• To explore the relationship between hemodynamic signals and initial dips.

Main Methods:

• Utilized a vector-based phase diagram incorporating a threshold from resting-state hemodynamics to identify initial dips.
• Applied an autoregressive moving average with exogenous signals (ARMAX) model for q-step-ahead prediction of dip occurrences.
• Acquired fNIRS signals from prefrontal and motor cortices during mental arithmetic and hand clenching tasks.

Main Results:

• The combined threshold criterion and ARMAX prediction method significantly reduced the time lag in initial dip detection.
• Achieved a delay time of approximately 0.9 seconds for initial dip detection.
• Demonstrated the feasibility of rapid initial dip detection using the proposed systematic method.

Conclusions:

• The developed method enables rapid detection of initial dips, overcoming limitations of previous approaches.
• This advancement holds significant potential for enhancing real-time brain-computer interfacing applications.
• The findings pave the way for more responsive and effective neurofeedback systems.