Laxmi Shaw

Efficacy of adaptive directed transfer function for neural connectivity estimation of EEG signal during meditation

Electroencephalogram (EEG) is widely used in cognitive science, neuroscience and physiological research. It is a good mean to observe cognitive response that depends on time. EEG has many advantages over other techniques owing to its non-invasiveness, low cost and high temporal resolution. But one of the major challenges of EEG signal study is the huge data dimensionality which makes signal processing and subsequent analysis an uphill task. The aim of this study is to obtain a model for better neural connectivity analysis which illustrates meditations dynamic mind-body response. Accordingly the EEG data is being collected during meditation (Kriya Yoga). In order to calculate and visualize the time-frequency representations of each electrode, a time varying Granger Causality based connectivity estimators named Directed Transfer Function (DTF) and adaptive DTF (ADTF) among all scalp electrodes have been computed in meditator group. The ADTF can be derived from the coefficients of a time-varying multivariate autoregressive (TVAR) model fitted to the data obtained during meditation. We define this time-varying measure of causality as the adaptive directed transfer function (ADTF) and compare its ability with the conventional DTF for meditator group. Both ADTF and Conventional DTF were calculated in meditator. The obtained simulation results of adaptive DTF and conventional DTF shows better neural connectivity and gives useful information in meditator group. However, to accomplish this task, surrogate data statistics has been used in both the mentioned models to validate the models. It was found that the ADTF has the capability to distinguish the dynamic changes in the primary source of the information outflow. The results obtained both by using ADTF and conventional DTF method were compared in meditator group subsequently.

Statistical features extraction for multivariate pattern analysis in meditation EEG using PCA

This work was undertaken to study the specific statistical features of EEG data collected during meditation (Kriya Yoga) and normal conditions. The meditation practice changes the attentional allocation in the human brain to visualize this; statistical features are carefully calculated from different wavelet coefficients to categorize two diverse groups (i.e. Meditators and Non-Meditators). The entire time series of EEG data divided into overlapping segments, and statistical parameters calculated for each of these segments. Instead of using all the data points, we used only a few higher order statistical measures such as variance, kurtosis, relative band energy, Shannon entropy, and Renyi entropy obtained from the data segments. A standard clustering technique, i.e. Principal Component Analysis (PCA) used to get the distinct pattern from the statistical features in EEG. In this paper, we presented a clustering paradigm that used for the pattern analysis between meditators and non-meditators. We measured the EEG signal using 64 channels, with some peripheral physiological measures. 23 participants with varying experience in meditation practice and ten non-meditators (control group) are considered to visualize underlying clusters within the statistical features.

Topographical assessment of neurocortical connectivity by using directed transfer function and partial directed coherence during meditation

Due to the presence of nonlinearity and volume conduction in electroencephalography (EEG), sometimes it’s challenging to find out the actual brain network from neurodynamical alteration. In this paper, two well-known time–frequency brain connectivity measures, namely partial directed coherence (PDC) and directed transfer function (DTF), have been applied to evaluate the performance analysis of EEG signals obtained during meditation. These measures are implemented to the multichannel meditation EEG data to get the directed neural information flow. Mostly the assessment of PDC and DTF is entirely subjective and there are probabilities to have erroneous connectivity estimation. To avoid the subjective evaluation, the performance results are compared in terms of absolute energy, signal-to-noise ratio (SNR) and relative SNR (R-SNR) scale. In most of the cases, the PDC result is found to be more efficient than DTF. The limitation of DTF and PDC in terms of the time-varying multivariate autoregressive (MVAR) model is highlighted. The time-varying MVAR model can track the neurodynamical changes better than any other method. In the present study, we would like to show that the PDC-based connectivity gives a better understanding of the non-symmetric relation in EEG obtained during Kriya Yoga meditation in comparison to DTF. However, it needs to be investigated further to warrant this claim.

Topographical pattern analysis using wavelet based coherence connectivity estimation in the distinction of meditation and non-meditation EEG

Classification of EEG signal involved in a particular cognitive activity has found many application in brain-computer interface (BCI). In specific, use of classification algorithms to highly multivariate non-stationary recordings like EEG is a challenging and promising task. This study investigated two sub-stantial novelty of the topics, (1) Distinction between meditation (Kriya Yoga) and non-meditation state allied EEG, (2) Characterization of the underlying mechanism of cognitive process that is associated with meditation using topographical analysis. The topographic wavelet coherence based brain connectivity between two different groups is shown. Two groups of data, one with 23 meditators (meditator group) and other with ten non-meditators (controlled group) are analyzed. The spatial distribution between two groups can be well distinguished by the topographical approach. The quantification has been done by the colour intensity embedded in the topographical plots. The wavelet coherence is found to be a different parameter to represent the distinctiveness between two groups. The time-frequency quantification regarding wavelet coherence spectrum is shown the unique patterns among meditators and non-meditators. Thus time-frequency based wavelet coherence has found to be an unusual brain pattern in the distinction between meditators and non-meditators.

Inferring functional brain connectivity estimation of continuous and missing sample in meditation state-allied scalp EEG

The present study provides a new framework for comparing functional brain connectivity between a continuous and missing sample of meditative EEG signal. The EEG signal acquired during meditation (Kriya Yoga) and after the removal of motifs as EOG spikes, few significant parameters of functional connectivity have been found out. Three essential parameters, i.e. Clustering coefficient, Global efficiency, and Network density are calculated and compared, in both continuous and disrupted EEG data. The results are inferred from the meditation EEG data, and it has been validated in 23 meditators. The findings are presented as a case study on the neural connectivity basis of understanding meditative state in missing samples during the meditative state allied EEG.

A critical comparison between SVM and k-SVM in the classification of Kriya Yoga meditation state-allied EEG

Support vector machines (SVM) have become a gold standard method for the classification of brain signals. However, for highly nonlinear and non-stationary signals like Electroencephalography (EEG), conventional SVM is not sufficient to classify the different brain states associated with different cognitive activity. Brain state classification is a challenging task when using standard SVM. Thus, a Kernel-based SVM (k-SVM) has been undertaken in the present study for classification between non-meditation (controlled group) and meditation based EEG. The k-SVM is popularly known as a non-linear classifier. In the present work, a comparative study has been taken up to classify the resting brain state associated with Kriya Yoga meditation practice using SVM and Kernel-SVM (k-SVM). The EEG signals have been captured from ten non-meditators (control group) and 23 meditators group. The results of both SVM and k-SVM have been shown and compared in both the groups. Additionally, the average classification accuracy has been found to be 85.543% for SVM and 90.8259% for k-SVM. The obtained results show that the kernel-based SVM surpassed the conventional SVM in classifying the meditation and non-meditation allied EEG.