An empirical comparison of machine learning algorithms for the classification of brain signals to assess the impact of combined yoga and Sudarshan Kriya

Sharma, Himika; Raj, Rajnish; Juneja, Mamta

doi:10.1080/10255842.2021.1975682

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…[21] used the gamma-band entropy-based features and fed them through a Random Forest classifier to differentiate between meditators vs. non-meditators. [19] and [7] used numerous machine learning classifiers to discriminate between mental states. They concluded that machine learning classifiers used hand-crafted features did not capture the most optimum representation to decode EEG signals.…”

Section: Fig 1 Sustaining Mind-full Momentsmentioning

confidence: 99%

Classifying EEG Signals of Mind-Wandering Across Different Styles of Meditation

Chaudhary

Pandey

Miyapuram

et al. 2022

Lecture Notes in Computer Science

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In the modern world, it is easy to get lost in thought, partly because of the vast knowledge available at our fingertips via smartphones that divide our cognitive resources and partly because of our intrinsic thoughts. In this work, we aim to find the differences in the neural signatures of mind-wandering and meditation that are common across different meditative styles. We use EEG recording done during meditation sessions by experts of different meditative styles, namely shamatha, zazen, dzogchen, and visualization. We evaluate the models using the leave-oneout validation technique to train on three meditative styles and test the fourth left-out style. With this method, we achieve an average classification accuracy of above 70%, suggesting that EEG signals of meditation techniques have a unique neural signature across meditative styles and can be differentiated from mind-wandering states. In addition, we generate lower-dimensional embeddings from higher-dimensional ones using t-SNE, PCA, and LLE algorithms and observe visual differences in embeddings between meditation and mind-wandering. We also discuss the general flow of the proposed design and contributions to the field of neurofeedback-enabled mind-wandering detection and correction devices.

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Section: Fig 1 Sustaining Mind-full Momentsmentioning

confidence: 99%

Classifying EEG Signals of Mind-Wandering Across Different Styles of Meditation

Chaudhary

Pandey

Miyapuram

et al. 2022

Lecture Notes in Computer Science

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“…Different classifiers, including decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and ensemble classifier (EC), are employed in the meditation study to distinguish meditative and nonmeditative states. [24]. Therefore, the purpose of this study is to make an objective measurement of HM by computing functional connectivity characteristics as a feature and selecting the best classifier that could distinguish between LTM, STM, and NM.…”

Section: Introductionmentioning

confidence: 99%

Effect of Heartfulness Meditation Among Long-Term, Short-Term and Non-meditators on Prefrontal Cortex Activity of Brain Using Machine Learning Classification: A Cross-Sectional Study

Shrivastava¹,

Singh²,

Krishna³

et al. 2023

Cureus

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BackgroundMeditation is a mental practice with health benefits and may increase activity in the prefrontal cortex of the brain. Heartfulness meditation (HM) is a modified form of rajyoga meditation supported by a unique feature called "yogic transmission." This feasibility study aimed to explore the effect of HM on electroencephalogram (EEG) connectivity parameters of long-term meditators (LTM), short-term meditators (STM), and non-meditators (NM) with an application of machine learning models and determining classifier methods that can effectively discriminate between the groups. Materials and methodsEEG data were collected from 34 participants. The functional connectivity parameters, correlation coefficient, clustering coefficient, shortest path, and phase locking value were utilized as a feature vector for classification. To evaluate the various states of HM practice, the categorization was done between (LTM, NM) and (STM, NM) using a multitude of machine learning classifiers. ResultsThe classifier's performances were evaluated based on accuracy using 10-fold cross-validation. The results showed that the accuracy of machine learning models ranges from 84% to 100% while classifying LTM and NM, and accuracy from 80% to 93% while classifying STM and NM. It was found that decision trees, support vector machines, k-nearest neighbors, and ensemble classifiers performed better than linear discriminant analysis and logistic regression. ConclusionThis is the first study to our knowledge employing machine learning for the classification among HM meditators and NM The results indicated that machine learning classifiers with EEG functional connectivity as a feature vector could be a viable marker for accessing meditation ability.

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