Work

Wavelet-Based EEG classification of neural responses to applied pressure physiotherapy

Year: 2025

Type: article

Abstract: The current research uses EEG signal analysis to investigate how Applied Pressure Physiotherapy (APP) modulates brain activity. Before and after APP sessions, 15 volunteers had their EEGs recorded. Prior to Maximal-Overlap Discrete Wavelet Packet Transform (MDWP) decomposition spanning the Delta, Theta, Alpha, Beta, and Gamma bands, pre-processing comprised band-pass filtering (0.5–40 Hz) and artifact removal. Significant variations in power were observed after APP: Alpha power increased by 4.8 %, which indicates a more alert but relaxed condition, whilst Delta and Theta bands declined with 9.4 % and 7.8 %, respectively. Decreased cognitive stress is further demonstrated by beta and gamma power drops for 9.8 % and 6.7 %, respectively. Neural stability improvements post APP have been observed by Hjorth measures, such as Activity (−11.5 %), Mobility (−45.3 %), as well as Complexity (−41.5 %). Utilizing parameters like Mean Curve Length (MCL), classification analyses with Linear Discriminant Analysis (LDA) and Random Forest (RF) acquired excellent precision (99.97 %). These results emphasize APP’s viability as a non-invasive treatment promoting mental wellness and cognitive improvement by demonstrating that it induces a relaxed mental state.

Source: Egyptian Informatics Journal

DOI: 10.1016/j.eij.2025.100855

Authors: Kumar Avinash Chandra, Prabhat Kumar Upadhyay, Pankaj Kumar

Institutions: Birla Institute of Technology, Mesra, Manipal Academy of Higher Education

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,200