PLV/PAC Feature Extraction Units for Implantable Neural Interfaces: Review | ||
| Aswan University Journal of Sciences and Technology | ||
| Volume 4, Issue 1, March 2024, Pages 72-79 PDF (727.65 K) | ||
| Document Type: Original papers | ||
| DOI: 10.21608/aujst.2024.258922.1077 | ||
| Authors | ||
| El-Sayed A. M. Hasaneen* 1; Hoda M. Yassin2 | ||
| 1Faculty of Engineering Aswan University | ||
| 2Faculty of Engineering Aswan University | ||
| Abstract | ||
| In neuroscience, synchronization between brain regions is quantified with phase locking value (PLV) and phase-amplitude coupling (PAC). PLV is a statistic feature that measures the level of phase synchronization between two signals within the same frequency bands by a vector whose magnitude represents the level of synchronization by a value between zero and one. Complex signal extraction involves filtering the input signal into different frequency bands and obtaining the real and imaginary components of each band. Fourier Transform (FT), ShortTime Fourier Transform (STFT), Morlet Wavelet (MWT), and Band-pass filtering followed by the Hilbert Transform (BPFH) are conventional techniques for complex signal extraction . Neural interfaces shows promise in treating neurological conditions such as Epilepsy, Depression, and Parkinson's disease. To enable fully implantable treating interfaces, efficient oscillatory feature extraction units are required. This article explores different techniques suggested for extracting phase locking value (PLV) and phase amplitude coupling (PAC) features. Additionally, the article provides an overview of the current state-of-the-art units and highlights their limitations. | ||
| Keywords | ||
| Feature extraction; oscillatory synchronization; phase locking value (PLV); phase-amplitude coupling (PAC) | ||
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