EEG Processing using LPC-based Methods

This project introduced a novel time-resolved spectral analysis method Linear Predictive Coding Pole Processing (LPCPP), specifically designed for EEG signal analysis. LPCPP offers high noise tolerance and superior time-frequency resolution, making it particularly effective in capturing instantaneous frequency variations in complex signals. Beyond EEG analysis, the method holds promise for integration into AI systems and diverse applications, including speech processing, mechanical vibration analysis, seismographic monitoring, and image analysis, with strong potential to advance real-world Human-Computer Interaction (HCI) technologies.

Related Publications

1. Jin Xu . "Time-Resolved Method for Spectral Analysis based on Linear Predictive Coding, with Application to EEG Analysis" Technological University Dublin PhD Thesis, 2022. 📄

2. Jin Xu, Mark Davis, Ruairí de Fréin. "An LPC pole processing method for enhancing the identification of dominant spectral features" Electronics Letters, 2021. 📄

3. Jin Xu, Mark Davis, Ruairí de Fréin. "Dominant frequency component tracking of noisy time-varying signals using the linear predictive coding pole processing method" Electronics Letters, 2021. 📄

4. Jin Xu, Mark Davis, Ruairí de Fréin. "A Linear Predictive Coding Filtering Method for the Time-resolved Morphology of EEG Activity" 32nd Irish Signals and Systems Conference (ISSC), 2021. 📄

5. Jin Xu, Mark Davis, Ruairí de Fréin. "New Robust LPC-Based Method for Time-resolved Morphology of High-noise Multiple Frequency Signals" 31st Irish Signals and Systems Conference (ISSC), 2020. 📄

6. Jin Xu, Mark Davis, Ruairí de Fréin. "A Robust LPC Filtering Method for Time-Resolved Morphology of EEG Activity Analysis" 26th Annual Conference of the Section of Bioengineering of the Royal Academy of Medicine in Ireland, 2020. 📄