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Articles linked to this work by shared authors, journal, and citation graph.

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Machine Learning-Based Comparative Analysis of Subject-Independent EEG Data Classification Across Multiple Meditation

Nalinda D Liyanagedera1,2, Corinne A Bareham3, Heather Kempton4

  • 1School of Mathematical and Computational Sciences, Massey University, Palmerston North 4410, New Zealand.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

Subject-independent electroencephalography (EEG) classification of loving-kindness meditation (LKM) and non-meditation is feasible. Machine learning models achieved moderate accuracy, with combined CSP + STFT features and more training sessions improving results.

Keywords:
BCI (brain computer interface)CSP (Common Spatial Pattern)EEG (Electroencephalography)STFT (Short-Time Fourier Transform)classificationmachine learningmeditationmultiple sessionneural networksubject independence

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Cognitive Science

Background:

  • Electroencephalography (EEG) is a key tool for studying brain activity during meditation.
  • Previous research focused on intra-subject classification, limiting real-world applications.
  • Subject-independent classification is crucial for developing practical brain-computer interfaces (BCIs).

Purpose of the Study:

  • To investigate the feasibility of subject-independent, multiple-session EEG classification for loving-kindness meditation (LKM) and non-meditation.
  • To compare the performance of different BCI pipelines (CSP, STFT, CSP + STFT) for this classification task.
  • To evaluate the impact of varying the number of training sessions on classification accuracy.

Main Methods:

  • Utilized EEG data from 12 participants across multiple sessions, comparing LKM (Self and Other) with non-meditation.
  • Employed three BCI pipelines involving feature extraction (CSP, STFT, CSP + STFT) and neural network classification.
  • Implemented a subject-independent approach by pooling data and randomly selecting sessions for training and testing, with repeated trials for generalization.

Main Results:

  • Mean classification accuracies were lower in subject-independent compared to intra-subject studies.
  • The CSP + STFT pipeline generally outperformed CSP or STFT alone (83.3% of instances).
  • Increasing the number of training session pairs improved classification accuracy in most cases (75.0% of instances).

Conclusions:

  • Subject-independent, multiple-session EEG classification of meditation states is feasible under specific conditions.
  • The CSP + STFT feature extraction method and increased training data show promise for enhancing accuracy.
  • Findings support the development of subject-independent algorithms for guiding meditation practices.