Krishna Prasad Miyapuram

• Ph.D.
• Professor (Associate) at Indian Institute of Technology Gandhinagar

Research in music perception and brain activity has led to the development of Music Brain-Computer Interface Systems. While previous studies have focused on aspects such as song identification, stimulus-response correlation, and inter-subject correlations, they have often overlooked the understanding of individual differences in subjective experien...

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 dif...

Over 1 in 6 people around the world face significant disabilities, and around half of them have limb disabilities. Brain-computer interfaces using EEG signals can help us create paradigms for the rehabilitation of patients with stroke, spinal cord injury, muscle degeneration, and so on. This study aims to classify neural signatures of palm open vs....

Decisions are driven both by sensory evidence that provides objective information as well as the anticipated outcomes and their corresponding subjective valuation. In this study, temporal dynamics of decision making are explored using an EEG study by separating different timepoints viz., reward information, stimulus onset, and feedback. We found th...

Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-Computer Interface (BCI) technology. The recent progress in deep learning has boosted the study area of synthesizing images from brain signals using Generative Adversarial Networks (GAN). In this work, w...

Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasa s. Rasa —as opposed to a pure emotion—is defined as a superposition of certain transitory, dominant, and temperamental e...

p>Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their b...

p>Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their b...

The article provides an open-source Music Listening- Genre (MUSIN-G) EEG dataset which contains 20 participants’ continuous Electroencephalography responses to 12 songs of different genres (from Indian folk music to Goth Rock to western electronic), along with their familiarity and enjoyment ratings. The participants include 16 males and 4 females,...

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...

Data Description: The Institute Ethical Committee (IEC) of Indian Institute of Technology, Gandhinagar, approved this study. Prior to conducting experiments, all of the participants provided informed consent. Participants: The study involved 20 healthy (mean age: 26 years, 16 males, 4 females), right-handed students from Indian Institute of Techno...

p>Studying brain waves elicited while listening to naturalistic music is a rapidly growing interdisciplinary research area encompassing experts from cognitive science, signal processing, and machine learning. Previous works have documented several perspectives, including correlating brain responses to stimulus features, inter-subject and time-varyi...

p>Studying brain waves elicited while listening to naturalistic music is a rapidly growing interdisciplinary research area encompassing experts from cognitive science, signal processing, and machine learning. Previous works have documented several perspectives, including correlating brain responses to stimulus features, inter-subject and time-varyi...

We examine user and song identification from neural (EEG) signals. Owing to perceptual subjectivity in human-media interaction, music identification from brain signals is a challenging task. We demonstrate that subjective differences in music perception aid user identification, but hinder song identification. In an attempt to address intrinsic comp...

World music is categorized into several genres based on complex features like harmony, rhythm, timbre, etc., present in a song. Music Information Retrieval (MIR) and neural entrainment studies have observed that neural responses to music encode features of the stimulus. The relationship between these music features and brain responses can be identi...

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 diffe...

Preference of consonant chords over dissonant chords, revealed through subjective ratings and pupil dilation responses. For more details : https://indico.aesthetics.mpg.de/event/2/attachments/26/30/Lange-Fink-MusicET2022-Full-Program-DIGITAL.pdf

There is abundant medical data on the internet, most of which are unlabeled. Traditional supervised learning algorithms are often limited by the amount of labeled data, especially in the medical domain, where labeling is costly in terms of human processing and specialized experts needed to label them. They are also prone to human error and biased a...

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human–robot interactions. We first introduce re...

Introduction: Resonance can be distinguished as a metaphor and physical mechanism. Neural resonance is a physical phenomenon that refers to the synchronization and amplification of brain oscillations to features of internal/external oscillators. Entrainment is a type of resonance, which can be further defined as external and internal. External entr...

Introduction: The neuroscientific study of meditation has shown enormous growth for its effects on multidimensional attributes, including neurophysiology, cognitive functioning, and phenomenological experience. Studies have shown that regular meditation results in long-term attentional regulatory and emotional benefits, alongside structural and fun...

In this study, we examined EEG analysis techniques across a variety of meditation traditions in order to identify reliable metrics that could be applied in meditation research, revealing how each tradition interprets eeg signals distinctly. The study’s potential is in its future neurotechnological innovations, to increase usage of meditation (by en...

Differential payoffs can bias simple perceptual decisions. Drift Diffusion models (DDM) have been successfully used to simultaneously model for response times (RTs) and accuracy of binary decisions. The DDM allows for identification of latent parameters that represent psychological processes underlying perceptual decisions. These parameters charact...

Neural oscillations are the rich source to understand cognition, perception, and emotions. Decades of research on brain oscillations have primarily discussed neural signatures for the western classification of emotions. Despite this, the Indian ancient treatise on emotions popularly known as Rasas has remained unexplored. In this study, we collecte...

Background Deep learning approaches for classification have the advantage of automatically learning the relevant features but require large amount of data for training. We test the efficacy of deep learning approach for prediction of risk factor for prodromal Alzheimer’s disease from whole brain MRI scans segmented into grey/white matter and CSF im...

Magnetic Resonance Imaging (MRI) is used extensively for the diagnosis of Alzheimer’s Disease (AD). Early detection of AD can help people with early intervention and alleviate the progression of disease symptoms. Previous studies have applied deep learning methods for computer-aided diagnosis of AD. In this present study, an efficient architecture...

Visual, audio, and emotional perception by human beings have been an interesting research topic in the past few decades. Electroencephalography (EEG) signals are one of the ways to represent human brain activity. It has been shown, that different brain networks correspond to processes corresponding to varieties of emotional stimuli. In this paper,...

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 medi...

Background Musical consonance is one of the basic tenets of western classical music and has been observed to be preferred over dissonance. This review explores previous literature corresponding to neural differences between consonant and dissonant musical chords, based on Event Related Potentials (ERP) and oscillations (ERO), neural entrainment, an...

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. Thi...

Mental imagery refers to percept-like experiences in the absence of sensory input. Brain imaging studies suggest common, modality-specific, neural correlates imagery and perception. We associated abstract visual stimuli with either visually presented or imagined monetary rewards and scrambled pictures. Brain images for a group of 12 participants we...

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. Thi...

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...

The brain is said to be asymmetrical when the two hemispheres are distinct from each other, structurally or functionally. Asymmetries are correlated with lateralized behavioral and functional features such as language, motor preferences, spatial and emotional processing, etc. It’s unclear how much and to which hemisphere inter-intra individual vari...

Learning sequential movements has been foundational to intelligent behavior. How we do acquire a new motor skill and the corresponding neural representations of motor sequence learning have been already established for a standard visuo-spatial map where the object itself is the target of action. Initial stages involve learning an effector-specific...

This research study aims to use machine learning methods to characterize the EEG response to music. Specifically, we investigate how resonance in the EEG response correlates with individual aesthetic enjoyment. Inspired by the notion of musical processing as "resonance", we hypothesize that the intensity of an aesthetic experience is based on the d...

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data pro...

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...

This research study aims to use machine learning methods to characterize the EEG response to music. Specifically, we investigate how resonance in the EEG response correlates with individual aesthetic enjoyment. Inspired by the notion of musical processing as resonance, we hypothesize that the intensity of an aesthetic experience is based on the deg...

The music signal comprises of different features like rhythm, timbre, melody, harmony. Its impact on the human brain has been an exciting research topic for the past few years. EEG signal primarily characterizes brain activity and carries vital information of visual, sensational and auditory perception of a human being. Leveraging the recent advanc...

Neuromarketing aims to understand consumer behavior using neuroscience. Brain imaging tools such as EEG have been used to better understand consumer behavior that goes beyond self-report measures which can be a more accurate measure to understand how and why consumers prefer choosing one product over another. Previous studies have shown that consum...

There is a sudden surge to model human behavior due to its vast and diverse applications which includes modeling public policies, economic behavior and consumer behavior. Most of the human behavior itself can be modeled into a choice prediction problem. Prospect theory is a theoretical model that tries to explain the anomalies in choice prediction....

In this work, we construct functional networks of the human brain using the coherence measure on the EEG time-series data, in response to external audio-visual stimuli. These stimuli were nine different movie clips selected to evoke different emotional states. The constructed networks for each emotion were characterized using network measures such...

We usually select the most suitable option from available alternatives to interact with the environment for our well-being. A successful interaction with the environment warrants continuous accumulation and interpretation of sensory information, and making a choice to guide an action based on the sensory evidence gathered and the prior knowledge. H...

In this work, we construct functional networks of the human brain using the coherence measure on the EEG time-series data, in response to external audiovisual stimuli. These stimuli were nine different movie clips selected to evoke different emotional states. The constructed networks for each emotion were characterized using network measures such a...

In this work, we construct functional networks of the human brain using the coherence measure on the EEG time-series data, in response to external audio-visual stimuli. These stimuli were nine different movie clips selected to evoke different emotional states. The constructed networks for each emotion were characterized using network measures such...

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data pro...

Our understanding of the decisions made under scenarios where both descriptive and experience-based information are available is very limited. Underweighting of small probabilities was observed in the gain domain when both description and experience were provided. The divergence observed from the prospect theory suggests a need for a separate or mo...

EEG signal analysis is a powerful technique to decode the activities of the human brain. Emotion detection among individuals using EEG is often reported to classify people based on emotions. We questioned this observation and hypothesized that different people respond differently to emotional stimuli and have an intrinsic predisposition to respond....

Real-world information is primarily sensory in nature, and understandably people attach value to the sensory information to prepare for appropriate behavioral responses. This review presents research from value-based, perceptual, and social decision-making domains, so far studied using isolated paradigms and their corresponding computational models...

In Indian history of arts, Rasas are the aesthetics associated with any auditory, visual, literary or musical piece of art that evokes highly orchestrated emotional states. In this work, we study the functional response of the brain to movie clippings meant to evoke the Rasas through network analysis. We extract functional brain networks using cohe...

In Indian history of arts, Rasas are the aesthetics associated with any auditory, visual, literary or musical piece of art that evokes highly orchestrated emotional states. In this work, we study the functional response of the brain to movie clippings meant to evoke the Rasas through network analysis. We extract functional brain networks using cohe...

We have developed a convolutional neural network for the purpose of recognizing facial expressions in human beings. We have fine-tuned the existing convolutional neural network model trained on the visual recognition dataset used in the ILSVRC2012 to two widely used facial expression datasets - CFEE and RaFD, which when trained and tested independe...

ABSTRACT Determining functional networks of regions in the brain and understanding their activations in relation to functions of the brain in humans have been two of the important goals in the €eld of neuroimaging research. Here, we present an application of biclustering data mining technique on a text-mined neuroimaging data and use the clustering...

Decision making can be treated as a two-step process involving sensory information and valuation of various options. However, the integration of value and sensory information at a neural level is still unclear.We used electroencephalography (EEG) to investigate the effect of reward information on perceptual decision making using two- alternative di...

Identifying neural correlates of value and perceptual-based decision making has been a major challenge. Previous studies have dissociated early and late components in a perceptual decision-making task. However, the integration of value and sensory information at a neural level is still unclear.We used electroencephalography (EEG) to investigate the...

Meta-analysis: Geee V. Glass 19999: Meta-analysis refers to the aaalysis of aalyses. Neurosynth (http://neurosynth.org/) provides us with forward inference (P(activation|term)), reverse inference (P(term| activation)) and allows mapping between neural and cognitive functions. Topic mapping (Poldrack et al, 2012): It applies data mining techniques...

Everyday life involves decision making situations of various kinds. Decision making research so far has been investigated separately in the domains of perceptual, value-based, and social domains. One view is that we represent various alternatives through their subjective values. Accordingly, the neural substrates of decision making would involve a...

Abstract: Introduction: The ancient Indian aesthetic theory, which is still widely followed in classical Indian performing arts, classifies emotions into nine different emotional states or Rasas which are: Sringaram (love, attractiveness), Hasyam (laughter, mirth, comedy), Raudram (anger, fury), Veeram (Heroism), Adbhutam (wonder, surprise, amazeme...

The goal of this study is to review the behavioral and neurophysiological studies of how economic information affects perceptual decision making and to bridge the results at two levels using computational models. Finally, to consider moving towards the question that researchers in both the fields are eager to answer: where in the brain are the deci...

In everyday life, we encounter many such situations in which we need to make decisions. A major gap in decision-making research so far is that decision-making paradigms are often limited to specific domains, such as perceptual, value-based, or social stimuli. We use two-alternative forced choice experiment as a unified paradigm to present different...

This paper presents a Graph clustering method for identifying functionally similar key concepts for meta-analysis of brain imaging studies. We use an existing database of key concepts created by a large-scale automated text mining of brain imaging studies. The key concepts here refer to specific psychological terms of interest (for instance, 'decis...

Brain imaging using functional MRI allows us to understand brain function while participants are engaged in meaningful tasks. Traditionally the experimental paradigms have been limited to repeated presentation of stimuli to participants followed by a model-based analysis of the data. The Inter Subject Correlation (ISC) analysis allows a model-free...

Sense of agency refers to the sense of authorship of an action and its outcome. Sense of agency is often explained through computational models of motor control (e.g., the comparator model). Previous studies using the comparator model have manipulated action-outcome contingency to understand its effect on the sense of agency. More recent studies ha...

Functional neuroimaging offers huge amounts of data that require computational tools to help extract useful information about brain function. The ever increasing number of neuroimaging studies (above 5000 in 2012 alone) suggests the need for a meta-analysis of these findings. Meta-analysis is aimed at increasing the power and reliability of finding...

Mental imagery refers to percept-like experiences in the absence of sensory input. Brain imaging studies suggest common, modality-specific, neural correlates imagery and perception. We associated abstract visual stimuli with either visually presented or imagined monetary rewards and scrambled pictures. Brain images for a group of 12 participants we...

Functional neuroimaging studies have become quite popular for more than a decade now. One of the greatest benefits among others is the correlational mapping of the brain structures with their functions differ. However, these single functional neuroimaging studies cannot bring out aspects of data like testing existing hypotheses, correspondence acro...

Consumers make many decisions in everyday life involving finances, food, and health. It is known from behavioral economics research that people are often driven by short-term gratification, that is, people tend to choose the immediate, albeit smaller reward. But choosing the delayed reward, that is, delaying the gratification, can actually be benef...

The neuroscience of decision making is a rapidly evolving multidisciplinary research area that employs neuroscientific techniques to explain various parameters associated with decision making behavior. In this chapter, we emphasize the role of multiple disciplines such as psychology, economics, neuroscience, and computational approaches in understa...

People make many decisions throughout the day involving finances, food and health. Many of these decisions involve considering alternatives that will occur at some point in the future. Behavioural economics is a field that studies how people make these decisions (Camerer, 1999)[[Au: The reference “Camerer (1999)” is cited in the text but not listed...

Monetary rewards are uniquely human. Because money is easy to quantify and present visually, it is the reward of choice for most fMRI studies, even though it cannot be handed over to participants inside the scanner. A typical fMRI study requires hundreds of trials and thus small amounts of monetary rewards per trial (e.g. 5p) if all trials are to b...

Previous brain imaging studies investigating motor sequence complexity have mainly examined the effect of increasing the length of pre-learned sequences. The novel contribution of this research is that we varied the structure of complex visuo-motor sequences along two different dimensions using mxn paradigm. The complexity of sequences is increased...