Daisy Das

Meditation has increasingly been recognized as a helpful non-pharmacological intervention to treat psychological stress, anxiety, and depression during pregnancy—a period that plays a significant role in maternal and fetal health. Although very popular, there is very little scientific evidence on how brief audio meditation affects brain activity in pregnant women. This work reports an EEG-based method to objectively investigate the neural effect of short-term audio meditation in pregnant women with the aid of sophisticated signal decomposition and deep learning methods. Electroencephalogram (EEG) signals were acquired from pregnant women in various mental states: resting, meditation, and post-meditation. For improved feature representation, Variational Mode Decomposition (VMD) was used to decompose each EEG channel into five intrinsic mode functions, representing multi-resolution signal components. The IMFs were subsequently processed through a hybrid deep learning model integrating Convolutional Neural Networks (CNNs) for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal dynamics modeling. The test accuracy of the recommended VMD-CNN-BiLSTM architecture was 94.79%, and test loss was 0.2975, proving its high reliability in classifying EEG states during meditation. The model’s performance verifies its ability to extract frequency-specific and temporal features characteristic of short-term cognitive modulation. This study offers a new, objective framework to evaluate the effect of meditation during prenatal care and opens avenues toward personalized, brain-based mindfulness training in maternal care.

Background and Purpose: Electroencephalography (EEG) is a popular non-invasive method for studying brain dynamics because of its excellent temporal resolution. However, the non-stationarity, intersubject variability and class imbalance of EEG data, make it difficult to automatically discriminate between brain states that correspond to various cognitive or sensory circumstances. With the use of a deep hybrid convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks with an attention architecture intended to improve discriminative learning from wavelet-based time–frequency features, this study attempts to categorise EEG recordings into discrete brain states recorded prior to and during auditory (mantra) stimulation. Methods: Experienced practitioners (mean age: 37 ± 6 years; mean practice: 5 years) had their EEG data recorded in two different experimental settings: (a) When they were at rest before the auditory stimulus and (b) while they were listening to mantras. Each segment’s time–frequency representations were produced using wavelet transforms and fed into a hybrid model that combined convolutional, recurrent and attention layers. To guarantee steady convergence, adaptive learning rate scheduling and early stopping were used in the model optimisation process. Results: With CNN (76.92%), long short-term memory (LSTM) (75.30%), CNN+LSTM (84.62%) and CNN+BiLSTM (88.65%), baseline models performed moderately. The suggested CNN–BiLSTM–Attention model achieved an independent test accuracy of 99.46%, greatly outperforming all baselines. High discriminative capability was confirmed by the receiver operating characteristic (ROC) analysis, which produced an AUC near 1.0. Conclusion: The inclusion of convolutional, recurrent and attention methods greatly improves spatial–temporal feature learning, as demonstrated by the suggested framework’s ability to distinguish between resting and during mantra EEG states. These results demonstrate the model’s resilience and possible use in neurophysiological monitoring and real-time cognitive state detection.

Pregnancy is often a time of increased stress and anxiety, both psychologically and physically. More and more research is being done on the calming effects of relaxation techniques on the mother's brain. Passively listening to meditative mantras is one such method. This study investigates neuronal phase synchronization in three distinct cognitive states in pregnant women with their eyes closed: resting state (RS), mantra listening (M), and after mantra listening (AM). 32 scalp electrodes and a 128 Hz sampling rate were used to collect EEG data from 43 pregnant subjects. There were two 2-minute trials in each state. To assess the temporal synchrony of brain oscillations, the Inter-Trial Coherence (ITC), a phase-locking metric that quantifies the stability of neural phase over multiple trials, was computed. Bandpass filtering followed by Hilbert transform was used to assess ITC across the Theta (4–8 Hz), Alpha (8–13 Hz), and Beta (13–30 Hz) frequency bands. The Mantra condition had the greatest mean ITC, according to the results: 0.9117 (Theta), 0.8891 (Alpha), and 0.8083 (Beta). Conversely, the After-Mantra condition displayed moderate ITC levels of 0.6582 (Theta), 0.6510 (Alpha), and 0.6437 (Beta), whereas the Resting State produced 0.6392 (Theta), 0.6381 (Alpha), and 0.6368 (Beta). According to these results, passive mantra listening improves brain phase synchrony, especially in the lower frequency bands, and could be a useful non-invasive method of meditative pregnant relaxation.

Background: Stress is a major problem in today’s culture, especially for expectant mothers. Newer therapies that show promising avenues for the reduction of stress include sound therapy, yoga, mantra chanting, and meditation (M). Electroencephalography (EEG) electroencephalography plays an important role in understanding the relaxation effects caused by these practices. Purpose: This study looks at the immediate neuro-physiological effects of brief auditory stimuli on pregnant participants, focusing on EEG responses during and after meditation (AM). Unlike the literature that has focused on AM, the significance of EEG readings during M is emphasised. Methods: The new classification model, namely REA (R-score, EEG and Artificial Neural Network [ANN]) was employed for the analysis against the conventional approach of ANN. Results: The statistical feature analysis showed in M EEG more significant than AM EEG. The results indicated that short audio interventions can be used to induce relaxation, and the identified EEG channels of CFz (frontal midline), F8 (right frontal lobe), and CP2 (parietal lobe) resulted in 100% accuracy in recognising meditative states during M. This accuracy underlines the importance of these regions for the differentiation of the relaxation states. Conclusion: The present study offers novel insights into trimester-specific responses to brief M audio stimuli and highly underscores the crucial role of EEG monitoring in M. These results point out that short auditory interventions might serve as a useful tool in the realm of stress reduction in prenatal care and offer a future direction of research into the neuro-physiological effects of M.

Purpose: Electroencephalography (EEG) is a widely used non-invasive method to explore brain activity and cognitive states, including the effects of meditation. The potential of prenatal EEG to understand the neural mechanisms underlying maternal brain activity makes it an exciting field of study. However, research on prenatal EEG during different stages of meditation is currently lacking. Additionally, feature map generation and selection remain tedious and time-intensive, requiring careful identification of relevant features to capture both linear and non-linear insights from EEG data. Methods: This paper proposes a Tetra Feature Map (TFM) for analyzing prenatal EEG signals, enabling effective differentiation of meditative states. TFM integrates four key aspects of the data: frequency bands, time domains, statistical features, and entropy features along with geometrical features facilitating a comprehensive analysis of dynamic EEG behavior. The PEM-43 dataset was used to evaluate TFM, tested across 33 machine learning classifiers to assess its performance in categorizing different meditative states. Results: The study revealed that TFM achieved a test accuracy of 92%. Furthermore, TFM was compared with two other feature sets, the Statistical Feature Map (SFM) and the New Feature Map (NFM), which achieved classification accuracies of 88% and 89%, respectively. TFM demonstrated superior performance in classification accuracy. Conclusion: These findings indicate that TFM can effectively analyze prenatal EEG data with a high classification success rate. This marks a significant advancement in prenatal EEG research and its application in brain state classification, particularly in the context of meditation.

In neuroscience and clinical diagnostics, electroencephalography (EEG) is a crucial instrument for capturing neural activity. However, this signal is polluted by different artifacts like muscle activity, eye blinks, environmental interference, etc., which makes it more difficult to retrieve important information from the signal. Deep learning methods have demonstrated the potential to lower these artifacts and enhance the EEG’s quality in recent years. In this work, a novel deep learning method,“AnEEG” is presented for eliminating artifacts from EEG signal. The quantitative matrices NMSE, RMSE, CC, SNR and SAR are calculated to confirm the effectiveness of the proposed model. Through this process, it was found that the suggested model outperformed wavelet decomposition techniques. The model achieves lower NMSE and RMSE values, which indicates better agreement with the original signal. Achieving higher CC values means stronger linear agreement with the ground truth signals. Additionally, the model shows improvements in both SNR and SAR values. Overall, this suggested approach showcases promising results in improving the quality of EEG data by utilizing deep learning.