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Brain Informatics

ISSN 2198-4018

2 papers in the library · 8 citations · publishing 2023-2025

Papers

Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline

Brain Informatics February 8, 2025 Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton et al. 4 citations

A machine learning pipeline that fuses two feature-extraction methods—Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT)—classified loving-kindness meditation (LKM) versus non-meditation states from EEG data more accurately than either method alone. Using multiple sessions of EEG from 12 participants, the combined pipeline achieved an overall accuracy of 72.9%, compared to 67.1% for CSP alone and 67.8% for STFT alone. The highest mean accuracy for a single participant was 75.5% for LKM-Self versus non-meditation with five sessions, and the top individual accuracy reached 88.9%. Classification improved as the number of training sessions increased from two to four.

DOI: 10.1186/s40708-025-00251-4 (opens in new tab) Full text (opens in new tab) Meditation Pipeline software Electroencephalography Short-time fourier transform +7

Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions

Brain Informatics September 9, 2023 Nalinda D. Liyanagedera, Ali Abdul Hussain, Amardeep Singh et al. 4 citations

Classifying loving kindness meditation (LKM) from non-meditation using electroencephalography (EEG) data is possible for both single and multiple sessions. For a single session with 32 participants, classification accuracy reached about 99.5% for distinguishing meditation from pre-resting states. For 15 participants across five sessions, accuracy was about 83.6%. Common Spatial Patterns, a feature extraction method typically used in motor imagery brain-computer interfaces, was applied to meditation EEG data for the first time. Comparisons among four mind tasks—pre-resting, post-resting, LKM directed toward self, and LKM directed toward others—showed that pre-resting states were more easily distinguished from the other three, suggesting pre-resting has distinct neural features.

DOI: 10.1186/s40708-023-00204-9 (opens in new tab) Meditation Electroencephalography Session web analytics Pattern recognition psychology +5