No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Brain Connectivity Based Classification of Meditation Expertise
Abstract
Recent developments in neurotechnology effectively utilize the decades of neuroscientific findings of multiple meditation techniques. Meditation is linked to higher-order cognitive processes, which may function as a scaffold for cognitive control. In line with these developments, we analyze oscillatory brain activities of expert and non-expert meditators from the Himalayan Yoga tradition. We exploit four dimensions (Temporal, Spectral, Spatial and Pattern) of EEG data and present an analysis pipeline employing machine learning techniques. We discuss the significance of different frequency bands in relation with distinct primary 5 scalp brain regions. Functional connectivity networks (PLV) are utilized to generate features for classification between expert and non-expert meditators. We find (a) higher frequency β and γ oscillations generate maximum discrimination over the parietal region whereas lower frequency θ and α oscillations dominant over the frontal region; (b) maximum accuracy of over 90% utilizing features from all regions; (c) Quadratic Discriminant Analysis surpasses other classifiers by learning distribution for classification. Overall, this paper contributes a pipeline to analyze EEG data utilizing various properties and suggests potential neural markers for an expert meditative state. We discuss the implications of our research for the advancement of personalized headset design that rely on feedback on depth of meditation by learning from expert meditators.
... One conclusion which we can draw from these results is that all four EEG bands considered in this study have good information to discriminate between the meditative and resting states, and the EEG band that gives the best results may depend on the features extracted and the classifier used. Another explanation for this contradiction is based on a recent work by Pandey et al. [21] where the authors have shown that the discriminability of features corresponding to various EEG frequency bands for the task of classifying meditation expertise depends on the EEG channels used. The CSP-based architectures rely on channels that extremize the objective function in Eq. 1, whereas the SVD-NN architecture relies on all channels. ...
Three distinct architectures are proposed to distinguish the modified state of consciousness during Rajyoga meditation from the resting state of the brain. Systems that classify the EEG recorded during modified states of consciousness from the resting state EEG can be used to grade the depth of meditation and for classifying disorders of consciousness. In the first architecture, common spatial pattern (CSP) is employed for feature extraction and linear discriminant analysis (LDA) for classification. This CSP-LDA architecture exploits the differences between the variances of the EEG signals of the two classes. The second architecture uses CSP for feature extraction, LDA for dimensionality reduction, and long short-term memory for classification. This CSP-LDA-LSTM architecture models the binary classification problem as a sequence learning problem. The third architecture uses singular value decomposition to choose the most relevant part of the signal, which is classified using a shallow neural network. This SVD
... Recently, there has been a surge in the development of machine learning models for meditation due to the availability of wearable EEG headsets for consumer use. Identifying differences between expert and non-expert have been in the rise of exploration using machine learning with signal processing techniques [10,[23][24][25][26][27]. Pre-and post-changes after a few weeks of practice are the quickest way to observe the effects with interpretability. ...
... Pandey and Miyapuram describe a wavelet-based encoding of the oscillatory signature of meditators (Pandey & Miyapuram, 2020). In recent investigations, functional connectivity networks were examined to predict brain activity in meditators (Pandey et al., 2021). Convolutional neural networks are used to create a model that categorizes control and meditators' cognitive states (Pandey & Miyapuram, 2021b). ...
Research into the similarities and differences between various forms of meditation practice is still in its early stages. Here, utilizing functional connectivity and graph measures, we present our work examining three meditation traditions: Himalayan Yoga (HT), Isha Shoonya (SNY), and Vipassana (VIP). EEG activity of the meditative block is used to build functional brain connections to exploit the resulting networks between various meditation traditions and a control group. Support vector machine is employed for binary classification, and models are built with features generated via graph theory measures. We obtain maximum accuracy of 84.76% with gamma1, 90% with alpha, and 84.76% with theta in HT, SNY, and VIP, respectively. Our key findings involve (a) higher delta connectivity in Vipassana meditators, (b) synchronization of theta networks in the left hemisphere inspected to be stronger in the anterior frontal area across meditators, (c) greater involvement of gamma2 processing observed among Himalayan and Vipassana meditators, (d) increased left frontal activity contribution for all meditators in theta and gamma bands, and (e) modularity engaged extensively in gamma processing across all meditation traditions. Furthermore, we discuss the implication of this research for neurotechnology products to enable guided meditation among naive practitioners.
... We propose to build a Mindfulness Kit, and this work is the initial framework on which several components can be connected to build a prototype. Our previous studies have shown evidence of neural signatures associated with expert meditative states using machine, and deep learning techniques [9], [13], [14], [48]. These neural signatures may be utilized to train a naive practitioner to reach a mindful state. ...
... Machine Learning analysis was performed in python using the scikit-learn library [21]. Classifiers employed were K Nearest Neighbors (KNN), Support Vector Machine (SVC), Decision Tree Classifier, Random Forest Classifier, Multi-Layered Perceptron (MLP), Ada Boost Classifier, Gaussian Naive Bayes, and Quadratic Discriminant Analysis (QDA) classifiers and Fig. 3. Differences between Rapid and Slow Deep breathing these classifiers are found to be significant in various EEG literature [22], [23]. Similar to spectral analysis, binary classification was performed on five breathing pairs. ...
... This is a problem highlighted by Riberio et al., wherein they discuss a model that performs well but has learned the wrong representation [17]. Recent work [12] uses the functional connectivity between brain regions as features to understand the significance and contribution of each region to the generated EEG signal. Previously, feature engineering-based methods were used to feed input to machine learning classifiers with varying degrees of success. ...
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-one-out 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 neuro-feedback-enabled mind-wandering detection and correction devices.KeywordsMeditationMind-wanderingClassificationMachine learningDeep learningCognitionNeuro-feedbackEEG
The brain is an incredibly complex organ capable of perceiving and interpreting a wide range of stimuli. Depending on individual brain chemistry and wiring, different people decipher the same stimuli differently, conditioned by their life experiences and environment. This study’s objective is to decode how the CNN models capture and learn these differences and similarities in brain waves using three publicly available EEG datasets. While being exposed to a variety of media stimuli, each brain produces unique brain waves with some similarity to other neural signals to the same stimuli. However, to figure out whether our neural models are able to interpret and distinguish the common and unique signals correctly, we employed three widely used CNN architectures to interpret brain signals. We extracted the pre-processed versions of the EEG data and identified the dependency of time windows on feature learning for song and movie classification tasks, along with analyzing the performance of models on each dataset. While the minimum length snippet of 5 s was enough for the personalized model, the maximum length snippet of 30 s proved to be the most efficient in the case of the generalized model. The usage of a deeper architecture, i.e., DeepConvNet was found to be the best for extracting personalized and generalized features with the NMED-T and SEED datasets. However, EEGNet gave a better performance on the NMED-H dataset. Maximum accuracy of 69%, 100%, and 56% was achieved in the case of the personalized model on NMED-T, NMED-H, and SEED datasets, respectively. However, the maximum accuracies dropped to 18%, 37%, and 14% on NMED-T, NMED-H, and SEED datasets, respectively, in the generalized model. We achieved a 5% improvement over the state of the art while examining shared experiences on NMED-T. This marked the out-of-distribution generalization problem and signified the role of individual differences in media perception, thus emphasizing the development of personalized models along with generalized models with shared features at a certain level.
Introduction
This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP).
Methods
We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario.
Results
For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively.
Conclusion
Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.
With three decades of contemplative research, Meditation has been an effective tool to increase attentional engagement, well-being, and states of flow and be beneficial to practice in this COVID-19 situation. But what is challenging, there is no feedback provided to the naive participants for his/her meditative performance, which can be frustrating and less rewarding. With recent advancements, EEG(Electroencephalogram) headset technology provides a mechanism for individuals to track his/her progress. However, processing those EEG signals and extracting features is crucial for classifying the different brain states, helping participants navigate their performance in a more customizable and flexible manner through feedback. Feature extraction plays an essential role in identifying the correct sets of features that provide a robust representation and derive a significant conclusion. We utilize fractal analysis, as this is well suited for the non-linear and non-stationary time series signals. Feature extraction is done by two fractal methods, which are Higuchi and Kat’z algorithms. Once features are extracted, we employ four machine learning classifiers to classify expert and non-expert meditators’ brain states. We further reduce the feature dimension by three techniques and later visualize the three components of expert and non-expert. We demonstrate the feature extraction using fractal analysis that further identifies the brain states associated with expert and non-expert meditators. This study contributes to the neural correlates of meditators. We can extend this work to understand the different meditation stages by identifying the features of each stage.
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.
If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it’s difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.
Meditation practices, originated from ancient traditions, have increasingly received attention due to their potential benefits to mental and physical health. The scientific community invests efforts into scrutinizing and quantifying the effects of these practices, especially on the brain. There are methodological challenges in describing the neural correlates of the subjective experience of meditation. We noticed, however, that technical considerations on signal processing also don't follow standardized approaches, which may hinder generalizations. Therefore, in this article, we discuss the usage of the electroencephalogram (EEG) as a tool to study meditation experiences in healthy individuals. We describe the main EEG signal processing techniques and how they have been translated to the meditation field until April 2020. Moreover, we examine in detail the limitations/assumptions of these techniques and highlight some good practices, further discussing how technical specifications may impact the interpretation of the outcomes. By shedding light on technical features, this article contributes to more rigorous approaches to evaluate the construct of meditation.
Cortical oscillations serve as an index of both sensory and cognitive processes and represent one of the most promising candidates for training and targeting the top-down mechanisms underlying executive functions. Research findings suggest that theta (θ) oscillations (3-7 Hz) recorded over frontal-midline (FMθ) electrodes are broadly associated with a number of higher-order cognitive processes and may serve as the mechanistic backbone for cognitive control. Frontal-midline theta (FMθ) has also been shown to inversely correlate with activity in the default mode network, a network in the brain linked to spontaneous thought processes such as mind-wandering and rumination. Our previous research found the presence of increased FMθ oscillations in expert meditation practitioners during reported periods of focused-attention meditation practice, when compared to periods of mind-wandering. In an effort to narrow the explanatory gap by connecting these neurophysiological features to the phenomenological nature of experience, we designed a methodologically novel and adaptive neurofeedback protocol with the aim of modulating FMθ while having meditation novice participants implement breath-focus strategies derived from focused-attention mediation practices. Participants who received the adaptive FMθ-meditation neurofeedback protocol were able to significantly modulate FMθ over frontal electrodes across eight sessions relative to their performance in session one. Neurofeedback recipients also showed significantly faster reaction times on the n-back working memory task assessed before and after the FMθ-meditation neurofeedback protocol. No significant differences in frontal theta activity or behavior were observed in the active control participants who received age and gender matched sham neurofeedback.
We address the hypothesis that the entropy of neural dynamics indexes the intensity and quality of conscious content. Previous work established that serotonergic psychedelics can have a dysregulating effect on brain activity, leading to subjective effects that present a considerable overlap with the phenomenology of certain meditative states. Here we propose that the prolonged practice of meditation results in endogenous increased entropy of brain oscillatory activity. We estimated the entropy of band-specific oscillations during the meditative state of traditions classified as 'focused attention' (Himalayan Yoga), 'open monitoring' (Vipassana), and 'open awareness' (Isha Shoonya Yoga). Among all traditions, Vipassana resulted in the highest entropy increases, predominantly in the alpha and low/high gamma bands. In agreement with previous studies, all meditation traditions increased the global coherence in the gamma band, but also stabilized gamma-range dynamics by lowering the metastability. Finally, machine learning classifiers could successfully generalize between certain pairs of meditation traditions based on the scalp distribution of gamma band entropies. Our results extend previous findings on the spectral changes observed during meditation, showing how long-term practice can lead to the capacity for achieving brain states of high entropy. This constitutes an example of an endogenous, self-induced high entropy state.
Discovering and exploiting shared, invariant neural activity in electroencephalogram (EEG) based classification tasks is of significant interest for generalizability of decoding models across subjects or EEG recording sessions. While deep neural networks are recently emerging as generic EEG feature extractors, this transfer learning aspect usually relies on the prior assumption that deep networks naturally behave as subject- (or session-) invariant EEG feature extractors. We propose a further step towards invariance of EEG deep learning frameworks in a systemic way during model training. We introduce an adversarial inference approach to learn representations that are invariant to inter-subject variabilities within a discriminative setting. We perform experimental studies using a publicly available motor imagery EEG dataset, and state-of-the-art convolutional neural network based EEG decoding models within the proposed adversarial learning framework. We present our results in cross-subject model transfer scenarios, demonstrate neurophysiological interpretations of the learned networks, and discuss potential insights offered by adversarial inference to the growing field of deep learning for EEG.
Background
Neural oscillations are often quantified as average power relative to a cognitive, perceptual, and/or behavioral task. This is commonly done using Fourier-based techniques, such as Welch’s method for estimating the power spectral density, and/or by estimating narrowband oscillatory power across trials, conditions, and/or groups. The core assumption underlying these approaches is that the mean is an appropriate measure of central tendency. Despite the importance of this assumption, it has not been rigorously tested.
New method
We introduce extensions of common approaches that are better suited for the physiological reality of how neural oscillations often manifest: as nonstationary, high-power bursts, rather than sustained rhythms. Log-transforming, or taking the median power, significantly reduces erroneously inflated power estimates.
Results
Analyzing 101 participants’ worth of human electrophysiology, totaling 3,560 channels and over 40 hours data, we show that, in all cases examined, spectral power is not Gaussian distributed. This is true even when oscillations are prominent and sustained, such as visual cortical alpha. Power across time, at every frequency, is characterized by a substantial long tail, which implies that estimates of average power are skewed toward large, infrequent high-power oscillatory bursts.
Comparison with existing methods
In a simulated event-related experiment we show how introducing just a few high-power oscillatory bursts, as seen in real data, can, perhaps erroneously, cause significant differences between conditions using traditional methods. These erroneous effects are substantially reduced with our new methods.
Conclusions
These results call into question the validity of common statistical practices in neural oscillation research.
Highlights
Analyses of oscillatory power often assume power is normally distributed.
Analyzing >40 hours of human M/EEG and ECoG, we show that in all cases it is not.
This effect is demonstrated in simple simulation of an event-related task.
Overinflated power estimates are reduced via log-transformation or median power.
Objective: Meditation is one type of mental training that has been shown to produce many cognitive benefits. Meditation practice is associated with improvement in concentration and reduction of stress, depression, and anxiety symptoms. Furthermore, different forms of meditation training are now being used as interventions for a variety of psychological and somatic illnesses. These benefits are thought to occur as a result of neurophysiologic changes. The most commonly studied specific meditation practices are focused attention (FA), open-monitoring (OM), as well as transcendental meditation (TM), and loving-kindness (LK) meditation. In this review, we compare the neural oscillatory patterns during these forms of meditation.
Method: We performed a systematic review of neural oscillations during FA, OM, TM, and LK meditation practices, comparing meditators to meditation-naïve adults.
Results: FA, OM, TM, and LK meditation are associated with global increases in oscillatory activity in meditators compared to meditation-naïve adults, with larger changes occurring as the length of meditation training increases. While FA and OM are related to increases in anterior theta activity, only FA is associated with changes in posterior theta oscillations. Alpha activity increases in posterior brain regions during both FA and OM. In anterior regions, FA shows a bilateral increase in alpha power, while OM shows a decrease only in left-sided power. Gamma activity in these meditation practices is similar in frontal regions, but increases are variable in parietal and occipital regions.
Conclusions: The current literature suggests distinct differences in neural oscillatory activity among FA, OM, TM, and LK meditation practices. Further characterizing these oscillatory changes may better elucidate the cognitive and therapeutic effects of specific meditation practices, and potentially lead to the development of novel neuromodulation targets to take advantage of their benefits.
One outstanding question in the contemplative science literature relates to the direct impact of meditation experience on the monitoring of internal states and its respective correspondence with neural activity. In particular, to what extent does meditation influence the awareness, duration and frequency of the tendency of the mind to wander. To assess the relation between mind wandering and meditation, we tested 2 groups of meditators, one with a moderate level of experience (non-expert) and those who are well advanced in their practice (expert). We designed a novel paradigm using self-reports of internal mental states based on an experiential sampling probe paradigm presented during ~1 h of seated concentration meditation to gain insight into the dynamic measures of electroencephalography (EEG) during absorption in meditation as compared to reported mind wandering episodes. Our results show that expert meditation practitioners report a greater depth and frequency of sustained meditation, whereas non-expert practitioners report a greater depth and frequency of mind wandering episodes. This is one of the first direct behavioral indices of meditation expertise and its associated impact on the reduced frequency of mind wandering, with corresponding EEG activations showing increased frontal midline theta and somatosensory alpha rhythms during meditation as compared to mind wandering in expert practitioners. Frontal midline theta and somatosensory alpha rhythms are often observed during executive functioning, cognitive control and the active monitoring of sensory information. Our study thus provides additional new evidence to support the hypothesis that the maintenance of both internal and external orientations of attention may be maintained by similar neural mechanisms and that these mechanisms may be modulated by meditation training.
This study aimed to verify the electrophysiological correlates of the changes in long-term regular meditators. We use modern techniques of high-resolution electroencephalography applied to slow potentials, power spectra, and potencies related to the events. To obtain encephalographic records, we use an assembly of 128 channels in 31 subjects (17 Soto Zen Buddhist meditators). The motivation of this study was to determine whether the induced beta power would present an increase in meditators as well as a decrease in induced theta/beta ratio in absolute and relative values. However, opposite to what we expected, no significant change was found in the beta frequency. In contrast, the main findings of the study were correlations between the frequency of weekly meditation practice and the increased theta induced relative power, increase of induced power ratio (ratio theta/beta), and increase of the ratio of induced relative powers (theta/beta ratio) during our task that featured an "adapted meditation," suggesting that the meditative state of "mindfulness" is much more related to the permittivity of "distractions" by the meditators, with a deliberate reduction of attention.
Scikit-learn is an increasingly popular machine learning li- brary. Written
in Python, it is designed to be simple and efficient, accessible to
non-experts, and reusable in various contexts. In this paper, we present and
discuss our design choices for the application programming interface (API) of
the project. In particular, we describe the simple and elegant interface shared
by all learning and processing units in the library and then discuss its
advantages in terms of composition and reusability. The paper also comments on
implementation details specific to the Python ecosystem and analyzes obstacles
faced by users and developers of the library.
We investigate the properties of the Phase Locking Value (PLV) and the Phase Lag Index (PLI) as metrics for quantifying interactions in bivariate local field potential (LFP), electroencephalography (EEG) and magnetoencephalography (MEG) data. In particular we describe the relationship between nonparametric estimates of PLV and PLI and the parameters of two distributions that can both be used to model phase interactions. The first of these is the von Mises distribution, for which the sample PLV is a maximum likelihood estimator. The second is the relative phase distribution associated with bivariate circularly symmetric complex Gaussian data. We derive an explicit expression for the PLV for this distribution and show that it is a function of the cross-correlation between the two signals. We compare the bias and variance of the sample PLV and the PLV computed from the cross-correlation. We also show that both the von Mises and Gaussian models are suitable for representing relative phase in application to LFP data from a visually-cued motor study in macaque. We then compare results using the two different PLV estimators and conclude that, for this data, the sample PLV provides equivalent information to the cross-correlation of the two complex time series.
This study sought to examine the effect of meditation experience on brain networks underlying cognitive actions employed during contemplative practice. In a previous study, we proposed a basic model of naturalistic cognitive fluctuations that occur during the practice of focused attention meditation. This model specifies four intervals in a cognitive cycle: mind wandering (MW), awareness of MW, shifting of attention, and sustained attention. Using subjective input from experienced practitioners during meditation, we identified activity in salience network regions during awareness of MW and executive network regions during shifting and sustained attention. Brain regions associated with the default mode were active during MW. In the present study, we reasoned that repeated activation of attentional brain networks over years of practice may induce lasting functional connectivity changes within relevant circuits. To investigate this possibility, we created seeds representing the networks that were active during the four phases of the earlier study, and examined functional connectivity during the resting state in the same participants. Connectivity maps were then contrasted between participants with high vs. low meditation experience. Participants with more meditation experience exhibited increased connectivity within attentional networks, as well as between attentional regions and medial frontal regions. These neural relationships may be involved in the development of cognitive skills, such as maintaining attention and disengaging from distraction, that are often reported with meditation practice. Furthermore, because altered connectivity of brain regions in experienced meditators was observed in a non-meditative (resting) state, this may represent a transference of cognitive abilities “off the cushion” into daily life.
We present the new effect of phase synchronization of weakly coupled self-sustained chaotic oscillators. To characterize this phenomenon, we use the analytic signal approach based on the Hilbert transform and partial Poincare maps. For coupled Rossler attractors, in the synchronous regime the phases are locked, while the amplitudes vary chaotically and are practically uncorrelated. Coupling a chaotic oscillator with a hyperchaotic one, we observe another new type of synchronization, where the frequencies are entrained, while the phase difference is unbounded. A relation between the phase synchronization and the properties of the Lyapunov spectrum is studied.
Long-term Vipassana meditators sat in meditation vs. a control rest (mind-wandering) state for 21 min in a counterbalanced design with spontaneous EEG recorded. Meditation state dynamics were measured with spectral decomposition of the last 6 min of the eyes-closed silent meditation compared to control state. Meditation was associated with a decrease in frontal delta (1-4 Hz) power, especially pronounced in those participants not reporting drowsiness during meditation. Relative increase in frontal theta (4-8 Hz) power was observed during meditation, as well as significantly increased parieto-occipital gamma (35-45 Hz) power, but no other state effects were found for the theta (4-8 Hz), alpha (8-12 Hz), or beta (12-25 Hz) bands. Alpha power was sensitive to condition order, and more experienced meditators exhibited no tendency toward enhanced alpha during meditation relative to the control task. All participants tended to exhibit decreased alpha in association with reported drowsiness. Cross-experimental session occipital gamma power was the greatest in meditators with a daily practice of 10+ years, and the meditation-related gamma power increase was similarly the strongest in such advanced practitioners. The findings suggest that long-term Vipassana meditation contributes to increased occipital gamma power related to long-term meditational expertise and enhanced sensory awareness.
Brain oscillatory activity is associated with different cognitive processes and plays a critical role in meditation. In this study, we investigated the temporal dynamics of oscillatory changes during Sahaj Samadhi meditation (a concentrative form of meditation that is part of Sudarshan Kriya yoga). EEG was recorded during Sudarshan Kriya yoga meditation for meditators and relaxation for controls. Spectral and coherence analysis was performed for the whole duration as well as specific blocks extracted from the initial, middle, and end portions of Sahaj Samadhi meditation or relaxation. The generation of distinct meditative states of consciousness was marked by distinct changes in spectral powers especially enhanced theta band activity during deep meditation in the frontal areas. Meditators also exhibited increased theta coherence compared to controls. The emergence of the slow frequency waves in the attention-related frontal regions provides strong support to the existing claims of frontal theta in producing meditative states along with trait effects in attentional processing. Interestingly, increased frontal theta activity was accompanied reduced activity (deactivation) in parietal-occipital areas signifying reduction in processing associated with self, space and, time.
We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
Several Convolutional Deep Learning models have been proposed to classify the cognitive states utilizing several neuro-imaging domains. These models have achieved significant results, but they are heavily designed with millions of parameters, which increases train and test time, making the model complex and less suitable for real-time analysis. This paper proposes a simple, lightweight CNN model to classify cognitive states from Electroencephalograph (EEG) recordings. We develop a novel pipeline to learn distinct cognitive representation consisting of two stages. The first stage is to generate the 2D spectral images from neural time series signals in a particular frequency band. Images are generated to preserve the relationship between the neighboring electrodes and the spectral property of the cognitive events. The second is to develop a time-efficient, computationally less loaded, and high-performing model. We design a network containing 4 blocks and major components include standard and depth-wise convolution for increasing the performance and followed by separable convolution to decrease the number of parameters which maintains the tradeoff between time and performance. We experiment on open access EEG meditation dataset comprising expert, nonexpert meditative, and control states. We compare performance with six commonly used machine learning classifiers and four state of the art deep learning models. We attain comparable performance utilizing less than 4% of the parameters of other models. This model can be employed in a real-time computation environment such as neurofeedback.
EEG oscillatory correlates of expert meditators have been studied in the time-frequency domain. Machine Learning techniques are required to expand the understanding of oscillatory signatures. In this work, we propose a methodological pipeline to develop machine learning models for the classification between expert and nonexpert meditative state. We carried out this study utilizing the online repository consisting of EEG dataset of 24 meditators that categorized as 12 experts and 12 nonexperts meditators. The pipeline consists of four stages that include feature engineering, machine learning classifiers, feature selection, and visualization. We decomposed signals using five wavelet families consisting of Haar, Biorthogonal(1.3-6.8), Daubechies( orders 2-10), Coiflet(orders 1-5), and Symlet(2-8), followed by feature extraction using relative entropy and power. We classified the meditative state between expert and non-expert meditators employing twelve classifiers to build machine learning models. Wavelet coefficients d8 shows the maximum classification accuracy in all the wavelet families. Wavelet orders Bior3.5 and Coif3 produce the maximum classification performance with the detail coefficient d8 using relative power. We have successfully classified the meditative state between expert and non-expert with 100% accuracy using d5,d6,d7,d8,a8 coefficients. Multi-Layer Perceptron and Quadratic Discriminant Analysis attain the highest accuracy. We have figured out the most discriminating channels during classification and reported 20 channels involving frontal, central and parietal regions. We plot the high dimensional structure of data by utilizing two feature reduction techniques PCA and t-SNE.
Rising from its contemplative and spiritual traditions, the science of meditation has seen huge growth over the last 30 years. This chapter reviews the classifications, phenomenology, neural correlates, and mechanisms of meditation. Meditation classification types are still varied and largely subjective. Broader models to describe meditation practice along multidimensional parameters may improve classification in the future. Phenomenological studies are few but growing, highlighting the subjective experience and correlations to neurophysiology. Oscillatory EEG studies are not conclusive likely due to the heterogeneous nature of the meditation styles and practitioners being assessed. Neuroimaging studies find common patterns during meditation and in long-term meditators reflecting the basic similarities of meditation in general; however, mostly the patterns differ across unique meditation traditions. Research on the mechanisms of meditation, specifically attention and emotion regulation is also discussed. There is a growing body of evidence demonstrating positive benefits from meditation in some clinical populations especially for stress reduction, anxiety, depression, and pain improvement, although future research would benefit by addressing the remaining methodological and conceptual issues. Meditation research continues to grow allowing us to understand greater nuances of how meditation works and its effects.
Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. However, where do EEG signals come from and what do they mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers. Neural oscillations are perhaps the best feature of EEG to use as anchors because oscillations are observed and are studied at multiple spatiotemporal scales of the brain, in multiple species, and are widely implicated in cognition and in neural computations.
This paper is concerned with the adaptive pinning synchronization problem of stochastic complex dynamical networks (CDNs). Based on algebraic graph theory and Lyapunov theory, pinning controller design conditions are derived, and the rigorous convergence analysis of synchronization errors in the probability sense is also conducted. Compared with the existing results, the topology structures of stochastic CDN are allowed to be unknown due to the use of graph theory. In particular, it is shown that the selection of nodes for pinning depends on the unknown lower bounds of coupling strengths. Finally, an example on a Chua's circuit network is given to validate the effectiveness of the theoretical results.
Introduction Instantaneous phase of signals and systems Phase
synchronization of chaotic self-sustained oscillators Looking for
synchronization phenomena in real data Conclusions
This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony.
The space around us is represented not once but many times in parietal cortex. These multiple representations encode locations and objects of interest in several egocentric reference frames. Stimulus representations are transformed from the coordinates of receptor surfaces, such as the retina or the cochlea, into the coordinates of effectors, such as the eye, head, or hand. The transformation is accomplished by dynamic updating of spatial representations in conjunction with voluntary movements. This direct sensory-to-motor coordinate transformation obviates the need for a single representation of space in environmental coordinates. In addition to representing object locations in motoric coordinates, parietal neurons exhibit strong modulation by attention. Both top-down and bottom-up mechanisms of attention contribute to the enhancement of visual responses. The saliance of a stimulus is the primary factor in determining the neural response to it. Although parietal neurons represent objects in motor coordinates, visual responses are independent of the intention to perform specific motor acts.
This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (,100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration.
API design for machine learning software: experiences from the Scikit-learn project
- L Buitinck
Buitinck, L., et al.: API design for machine learning software: experiences from the
Scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and
Machine Learning, pp. 108-122 (2013)
- P Pandey
- K P Miyapuram
Pandey, P., Miyapuram, K.P.: BRAIN2DEPTH: lightweight CNN model for classification of cognitive states from EEG recordings. arXiv preprint arXiv:2106.06688
(2021)