Key result
SVM outperforms other machine learning classifiers for decoding relaxation states from EEG data across postures.
Why the study?
Earlier studies largely focused on eyes-open versus eyes-closed states, leaving the electroencephalography relaxation correlates between supine and sitting postures incompletely understood.
Can machine learning classifiers effectively evaluate EEG correlates of relaxation between supine and sitting postures?
Observational (n=1)
Can machine learning classifiers effectively evaluate EEG correlates of relaxation between supine and sitting postures?
Machine learning, particularly Support Vector Machine (SVM), can effectively classify relaxation states during different postures using EEG and ECG metrics.
Hypothesis-generating for EEG-ML relaxation assessment across postures; prospective validation needed before clinical use.
Background: Brain relaxation is attained using several techniques while sleep remains nature's ultimate remedy. Currently, various machine learning (ML) tools are applied to identify and understand the neural correlates of relaxation from the electroencephalography (EEG) signals. Majority of earlier studies focused on comparing power in the EEG bands during eyes-open and eyes-closed resting state paradigm to train the datasets. However, several Yogic practices are performed using sitting and supine positions. Purpose: This study was aimed to elucidate the relaxation correlates in EEG between supine and sitting position during eyes-closed condition using ML classifiers. Methods: EEG signals were recorded on five different days from O1, OZ, O2, C3, CZ, C4, F3, FZ and F4 brain region using nine unipolar electrodes for 25 minutes during eyes-closed supine and eyes-closed sitting postures each on, along with electrocardiogram (ECG) for heart rate variability (HRV) analysis in a healthy participant. Relaxation was assessed by extracting the relative power of the alpha and theta waves from the EEG data and corroborated with the alpha and theta lateralisation index (LI) and HRV parameters. These EEG metrics were analysed by leveraging ML classifiers (K-nearest neighbours (KNN), support vector machine(SVM), random forest (RF) and XGBoost) for relaxation states under sitting and supine states. Results: Out of all the used classifiers, performance indices of SVM excelled in classifying relaxation states from the EEG alpha and theta band data that was verified with the HRV data and correlated with LI. Conclusion: This study demonstrates that ML especially the SVM was effective in classifying the relaxation states during different postures from the EEG. LI and HRV metrics effectively decoded the underlying message in the EEG and ECG respectively.
No takes yet. Share an insight, caveat, or question.
George et al. (2025) conducted an observational in Healthy (n=1). Supine posture vs. Sitting posture was evaluated on Classification of relaxation states from EEG alpha and theta band data. Support vector machine (SVM) was the most effective machine learning classifier for decoding relaxation states from EEG data during supine and sitting postures.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: