English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Phantom Limb Pain (PLP) is a chronic condition frequent among individuals with acquired amputation. PLP has been often investigated with the use of functional MRI focusing on the changes that take place in the sensorimotor cortex after amputation. In the present study, we investigated whether a different type of data, namely electroencephalographic (EEG) recordings, can be used to study the condition. We acquired resting state EEG data from people with and without PLP and then used machine learning for a binary classification task that differentiates the two. Common Spatial Pattern (CSP) decomposition was used as the feature extraction method and two validation schemes were followed for the classification task. Six classifiers (LDA, Log, QDA, LinearSVC, SVC and RF) were optimized through grid search and their performance compared. Two validation approaches, namely all-subjects validation and leave-one-out cross-validation (LOOCV), resulted in high classification accuracy. Most notably, the 93.7% accuracy achieved with SVC in LOOCV holds promise for good diagnostic capabilities using EEG biomarkers. In conclusion, our findings indicate that EEG data is a promising target for future research aiming at elucidating the neural mechanisms underlying PLP and its diagnosis.

Common Spatial Pattern EEG decomposition for Phantom Limb Pain detection