Modulation of EEG by Slow-Symmetric Breathing incorporating Breath-Hold.

The interplay between the brain and lungs involves intricate physiological mechanisms operating bi-directionally. Volitional breathing, unlike spontaneous breathing, offers various benefits with potential therapeutic effects. Volitional breathing involves many variables, such as breathing rate (BR) and breathing patterns.

Brain-scale theta band functional connectome as signature of slow breathing and breath-hold phases.

The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold. The variations in the electroencephalogram (EEG) based functional connectivity (FC) of the human brain have been investigated during attentive breathing at 2 cycles per minute (cpm). The study presents its novelty through three main aspects.

Functional reorganization of the brain in distinct frequency bands during eyes-open meditation.

Meditation is a self-regulatory process practiced primarily to reduce stress, manage emotions and mental health. The objective of this work is to study the information exchange between electrodes within and across the hemispheres during meditation using functional connectivity (FC) measures. We investigate the changes in the coherence between EEG electrode pairs during the meditation with open eyes practiced by long-term Brahmakumaris Rajyoga meditators and during listening to music by controls as the comparable task.

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness.

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface.

Modality-Specific Feature Selection, Data Augmentation and Temporal Context for Improved Performance in Sleep Staging.

This work attempts to design an effective sleep staging system, making the best use of the available signals, strategies, and features in the literature. It must not only perform well on different datasets comprising healthy and clinical populations but also achieve good accuracy in cross-dataset experiments. Toward this end, we propose a model comprising multiple binary classifiers in a hierarchical fashion, where, at each level, one or more of EEG, EOG, and EMG are selected to best differentiate between two sleep stages.

Classification of Phonological Categories in Imagined Speech using Phase Synchronization Measure.

Phonological categories in articulated speech are defined based on the place and manner of articulation. In this work, we investigate whether the phonological categories of the prompts imagined during speech imagery lead to differences in phase synchronization in various cortical regions that can be discriminated from the EEG captured during the imagination. Nasal and bilabial consonant are the two phonological categories considered due to their differences in both place and manner of articulation.

Brain Functional Connectivity as Biomarker for Propofol-Induced Alterations of Consciousness.

Understanding neural correlates of consciousness and its alterations poses a grand challenge for modern neuroscience. Even though recent years of research have shown many conceptual and empirical advances, the evolution of a system that can track anesthesia-induced loss of consciousness is hindered by the lack of reliable markers. The work presented herein estimates the functional connectivity (FC) between 21 scalp electroencephalogram (EEG) recordings to evaluate its utility in characterizing changes in brain networks during propofol sedation.

Midline EEG Functional Connectivity As Biomarker for Conscious States in Sleep and Wakefulness.

Functional connectivity (FC) between different cortical regions of the brain has long been hypothesized to be necessary for conscious states in several modeling and empirical studies. The work presented herein estimates the FC between two bipolar midline electroencephalogram (EEG) recordings to evaluate its utility in discriminating consciousness levels across wakefulness and sleep. Consciousness levels were defined as Low, Medium, and High depending upon the ability of a subject to self-report their experiences at a later stage.

Increased Entropy of Gamma Oscillations in the Frontal Region during Meditation.

Meditation practices are considered mental training and have increasingly received attention from the scientific community due to their potential psychological and physical health benefits. We compared the EEG data recorded from long-term rajayoga practitioners during different meditative and non-meditative periods. Minimum variance modified fuzzy entropy (MVMFE) is computed for each EEG band for all channels of a given lobe.

Can we identify the category of imagined phoneme from EEG?

Phonemes are classified into different categories based on the place and manner of articulation. We investigate the differences between the neural correlates of imagined nasal and bilabial consonants (distinct phonological categories). Mean phase coherence is used as a metric for measuring the phase synchronisation between pairs of electrodes in six cortical regions (auditory, motor, prefrontal, sensorimotor, so-matosensory and premotor) during the imagery of nasal and bilabial consonants.

Decoding Covert Speech From EEG-A Comprehensive Review.

Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework.

Electrophysiological and Neuroimaging Studies - During Resting State and Sensory Stimulation in Disorders of Consciousness: A Review.

A severe brain injury may lead to a disorder of consciousness (DOC) such as coma, vegetative state (VS), minimally conscious state (MCS) or locked-in syndrome (LIS). Till date, the diagnosis of DOC relies only on clinical evaluation or subjective scoring systems such as Glasgow coma scale, which fails to detect subtle changes and thereby results in diagnostic errors. The high rate of misdiagnosis and inability to predict the recovery of consciousness for DOC patients have created a huge research interest in the assessment of consciousness.

VR Glasses based Measurement of Responses to Dichoptic Stimuli: A Potential Tool for Quantifying Amblyopia?

Amblyopia is a medical condition in which the visual inputs from one of the eyes is suppressed by the brain. This leads to reduced visual acuity and poor or complete loss of stereopsis. Conventional clinical tests such as Worth 4-dot test and Bagolini striated lens test can only detect the presence of suppression but cannot quantify the extent of suppression, which is important for identifying the effectiveness of treatments for amblyopia.

R-wave Amplitude Changes and Atypical Heart Rate Changes Accompanying Breath Hold During Low Breathing Rates.

This paper reports an interesting phenomenon that the amplitude of the QRS complex reduces during inhalation and increases during exhalation and the variation can exceed even 100% during very slow breathing rates (BR). The phenomenon has been consistent in all the nine normal male subjects we have studied with age ranging from 23 to 61 years. Further, at very low respiration rates which included breath holds both after inhalation and exhalation, there are highly significant second and third harmonics of the respiration frequency in the heart rate variability spectrum.

Intrinsic-cum-extrinsic normalization of formant data of vowels.

Using a known speaker-intrinsic normalization procedure, formant data are scaled by the reciprocal of the geometric mean of the first three formant frequencies. This reduces the influence of the talker but results in a distorted vowel space. The proposed speaker-extrinsic procedure re-scales the normalized values by the mean formant values of vowels.

Voice source characterization using pitch synchronous discrete cosine transform for speaker identification.

A characterization of the voice source (VS) signal by the pitch synchronous (PS) discrete cosine transform (DCT) is proposed. With the integrated linear prediction residual (ILPR) as the VS estimate, the PS DCT of the ILPR is evaluated as a feature vector for speaker identification (SID). On TIMIT and YOHO databases, using a Gaussian mixture model (GMM)-based classifier, it performs on par with existing VS-based features.

Estimation of voice-onset time in continuous speech using temporal measures.

This paper proposes an automatic acoustic-phonetic method for estimating voice-onset time of stops. This method requires neither transcription of the utterance nor training of a classifier. It makes use of the plosion index for the automatic detection of burst onsets of stops.

Detection of the closure-burst transitions of stops and affricates in continuous speech using the plosion index.

Automatic and accurate detection of the closure-burst transition events of stops and affricates serves many applications in speech processing. A temporal measure named the plosion index is proposed to detect such events, which are characterized by an abrupt increase in energy. Using the maxima of the pitch-synchronous normalized cross correlation as an additional temporal feature, a rule-based algorithm is designed that aims at selecting only those events associated with the closure-burst transitions of stops and affricates.

Low cost calibrated mechanical noisemaker for hearing screening of neonates in resource constrained settings.

Background & Objectives: There is a need to develop an affordable and reliable tool for hearing screening of neonates in resource constrained, medically underserved areas of developing nations. This study valuates a strategy of health worker based screening of neonates using a low cost mechanical calibrated noisemaker followed up with parental monitoring of age appropriate auditory milestones for detecting severe-profound hearing impairment in infants by 6 months of age.

Methods: A trained health worker under the supervision of a qualified audiologist screened 425 neonates of whom 20 had confirmed severe-profound hearing impairment.

Quality assessment in magnetic resonance images.

Assessing quality of medical images is critical because the subsequent course of actions depend on it. Extensive use of clinical magnetic resonance (MR) imaging warrants a study in image indices used for MR images. The quality of MR images assumes particular significance in the determination of their reliability for diagnostics, response to therapies, synchronization across different imaging cycles, optimization of interventional imaging, and image restoration.

Composite MR image reconstruction and unaliasing for general trajectories using neural networks.

In rapid parallel magnetic resonance imaging, the problem of image reconstruction is challenging. Here, a novel image reconstruction technique for data acquired along any general trajectory in neural network framework, called "Composite Reconstruction And Unaliasing using Neural Networks" (CRAUNN), is proposed. CRAUNN is based on the observation that the nature of aliasing remains unchanged whether the undersampled acquisition contains only low frequencies or includes high frequencies too.

Improved k-t BLAST for fast fMR imaging.

A popular dynamic imaging technique, k-t BLAST (ktB) is studied here for fMR imaging. ktB utilizes correlations in k-space and time, to reconstruct the image time series with only a fraction of the data. The algorithm works by unwrapping the aliased Fourier conjugate space of k-t (y-f-space).

Spectral analysis of noise in the neonatal intensive care unit.

Objective: To perform spectral analysis of noise generated by equipments and activities in a level III neonatal intensive care unit (NICU) and measure the real time sequential hourly noise levels over a 15 day period.

Methods: Noise generated in the NICU by individual equipments and activities were recorded with a digital spectral sound analyzer to perform spectral analysis over 0.5 - 8 KHz.

Polarization-rich continuous wave direct imaging: modeling and visualization.

We report a study and comparison of continuous-wave, optical polarization difference imaging (PDI) and polarization modulation imaging (PMI) for imaging through scattering media. The problem is cast in the framework of a theoretical estimation, and the comparison is based on three visualization parameters, namely, the magnitude, the degree, and the orientation of the polarization. We show that PDI is superior in estimating the first two parameters in active imaging under specific conditions, while the PMI is suitable for passive imaging and is the only way to estimate polarization orientation.

Wavelet-based estimation of hemodynamic response function from fMRI data.

We present a new algorithm to estimate hemodynamic response function (HRF) and drift components of fMRI data in wavelet domain. The HRF is modeled by both parametric and nonparametric models. The functional Magnetic resonance Image (fMRI) noise is modeled as a fractional brownian motion (fBm).