Forecast Padding Enhances Accuracy and Robustness of EEG-Phase-Synchronized TMS

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Closed-loop neuromodulation offers personalized treatment for neuropsychiatric disorders by using real-time brain signals for precise stimulation.
• Clinical application is hindered by technical challenges, particularly in EEG-Phase-Synchronized Transcranial Magnetic Stimulation (TMS), due to filter edge effects impacting accuracy and biomarker detection.

Purpose of the Study:

• To address technical limitations in EEG-Phase-Synchronized TMS, specifically poor stimulation accuracy and inefficient biomarker detection caused by filter edge effects.
• To introduce a novel signal padding method, termed 'forecast padding,' inspired by predictive coding theory, to mitigate filter edge effects.
• To develop and validate a novel delay-relevant framework for quantifying improvements in real-world systems.

Main Methods:

• Proposed a novel signal padding technique called 'forecast padding' to counteract filter edge effects in signal processing.
• Developed a delay-relevant validation framework to assess the performance of the proposed method in real-world applications.
• Utilized experimental data from a real system to demonstrate the reliability and effectiveness of the validation framework and forecast padding.

Main Results:

• Forecast padding significantly improved stimulation accuracy in EEG-Phase-Synchronized TMS.
• The novel method led to more efficient biomarker detection, reducing the incidence of deadlock.
• The delay-relevant validation framework proved reliable in quantifying performance improvements.

Conclusions:

• Forecast padding effectively mitigates filter edge effects, enhancing the precision and efficiency of closed-loop neuromodulation systems.
• The developed validation framework provides a robust method for evaluating real-time signal processing improvements.
• Forecast padding has broad applicability beyond TMS, potentially benefiting various signal processing domains facing similar edge effect challenges.