Sandeep Bodda

@amrita.edu

Assistant Professor, Amrita Mind Brain Center
Amrita Mind Brain Center, Amrita Viswa Vidyapeetham

Sandeep Bodda


                        Download Compact PDF  
https://researchid.co/sandeepbodda
Dr. Sandeep Bodda currently serves as Assistant Professor at the Amrita Mind Brain Center in doctoral research focused on Decoding a hand-grasped movement using electroencephalography (EEG) to understand the neural signatures for movement-based tasks for rehabilitation. His research Interests spans across various domains, including Movement Control, Yoga Intervention Studies, and the Analysis of Cognitive Functions such as, Attention, Working Memory and Stress, using functional connectivity his research focuses delving into the intricate network interactions within the human brain during various activities such as movement control, yoga interventions, and cognitive processes.

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Cognitive Neuroscience, Behavioral Neuroscience
12

Scopus Publications

93

Scholar Citations

6

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Interconnections and global transitions among functional states encode activity-related dynamics as brain topology changes after yoga training
    Sandeep Bodda, Shyam Diwakar
    Scientific Reports, 2025
    With the emphasis on sustainable health, understanding the neural dynamics associated with sustainable practices such as widely practiced yoga has gained significant importance. In this work, we explored the underlying neural mechanisms of yoga training by means of electroencephalogram recordings. The EEG data was recorded before and after the yoga training of 13 participants, for a total of 39 trials, with each trial recorded on consecutive days. The temporal analysis was performed by means of microstates and the changes in the oscillatory rhythms were also evaluated via spectral and statistical analysis. Spectral analysis revealed changes in the oscillatory rhythms of β,γ,α,θ over the electrode regions of O2, P8 and FC6. An analysis of the changes in the temporal microstates revealed > 65% global variance in the topographic clusters, with a significant effect on the occurrence and time coverage parameters of the microstates before and after yoga training. This study highlights that yoga training significantly influences microstate dynamics associated with brain regions, including the visual network, insular cortex, and frontal gyrus, thereby potentially enhancing functions related to attention and cognitive decisions. These findings may suggest a multinetwork neurophysiological basis for the role of yoga in improving mental focus and adaptive decision processes.
  • Exploring EEG spectral and temporal dynamics underlying a hand grasp movement
    Sandeep Bodda, Shyam Diwakar
    Plos One, 2022
    For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex’s functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rdorder polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.
  • Computational Exploration of Neural Dynamics Underlying Music Cues among Trained and Amateur Subjects
    Akhil Kalariparambil Santhosh, Muralikrishna Sangilirajan, Nijin Nizar, Rakhi Radhamani, Dhanush Kumar, Sandeep Bodda, Shyam Diwakar
    Procedia Computer Science, 2020
    Exploring the neural basis of music perception and processing has become relevant in neuroscience research for studying human cognition and emotion. Recently many neuroimaging studies have seen brain structure differences in responses to various auditory cue, including music stimuli among diverse individuals. Research still lacks to correlate cortical dynamics in accordance with music familiarity among professionally trained musicians and amateur subjects. With this context, this short-term study reports on effectively recording and computational analysis of neural signals for understanding the cortical network correlates associated with different auditory cues (Western music, Indian classical music, Guitar music and stressor noise) on trained and amateur subjects. On the basis of cognitive battery score analysis, the study recruited 20 volunteering healthy subjects, and grouped as trained subjects (N=10), having music training and amateur subjects (N=10), having no prior music training. EEG recordings were taken for 7 minutes in an eye closed relaxed state, for different auditory stimuli, in a sound proof and dimly lit Indian laboratory settings. In both amateur and trained subjects, heat maps indicated cortical re-organization of ɵ, α, β, and γ rhythms when exposed to different auditory stimuli at similar time bins. Cortical mapping indicated frontal (F3, F4, F7, F8), temporal (T7, T8) and parietal (P7 and P8) as functional regions for processing an auditory information with improved cognitive, attention, memory and language processing skills among trained and amateur subjects in response to familiar music stimuli. With other biosignals analysis techniques and using electrical engineering principles, the study could be further expanded to explore brain anatomy with music therapy as a tool in clinical, therapeutic and community settings.
  • Computational analysis of EEG activity during stance and swing gait phases
    Sandeep Bodda, Sreelekshmi Maya, Manu Naryanan Em Potti, Uma Sohan, Yellapu Bhuvaneshwari, Rithul Mathiyoth, Shyam Diwakar
    Procedia Computer Science, 2020
    Abstract Analysis of cortical electroencephalography (EEG) patterns may suggest novel neural dynamics related to healthy and diseased gait which could be critical for assessing neurological disorders. However, the dynamics of cortical involvement in walking is not clearly understood. In this study, using non-invasive EEG, we recorded and analyzed the stance and swing gait cycle phases in healthy volunteering subjects. Extracted spectral and temporal features of gait data for right toe-off and heel strike were ranked based, using machine learning algorithms to identify patterns related to swing and stance. Increased beta rhythms, positive and negative motor potentials for stance and swing could be targeted as biosignatures discriminating gait cycle phases. Identifying such biosignatures help classify stance and swing phases and may be pertinent to preclinical studies and in resource-limited environments where expensive equipment may not be accessible.
  • Experimental Recording and Assessing Gait Phases Using Mobile Phone Sensors and EEG
    Abhijith Balachandran, Chaitanya Nutakki, Sandeep Bodda, Bipin Nair, Shyam Diwakar
    2018 International Conference on Advances in Computing Communications and Informatics Icacci 2018, 2018
    Human manner of walking characterized by kinematic parameters measure posture-gait control characterizing the dynamic changes in body parts with the involvement of multi-sensory patterns processed by different parts of the brain. In this study, low-cost sensors have been used to collect gait signals and identify the features responsible for differentiating the gait phases (swing/stance). Dataset was obtained for a total of 160 trails with 5 gait cycles per trail from healthy volunteers (n=20). Torque involved during progressive gait was also estimated to model regulation of the body for maintaining balance in gait and posture. Additionally, we also investigated EEG and gait correlates by identifying the brain regions that are active during movement initiation and during stance and swing (a progressive gait) using electroencephalography. While identifying key biomarkers relevant for posture control and gait, this could enhance low-cost detection of movement related diseases in technically challenged regions.
  • Modeling population network activity using LFPsim, spiking neurons and neural mass models
    Sandeep Bodda, Radhika K. Palathingal, Vinayak Sankar, Bipin Nair, Shyam Diwakar
    2017 International Conference on Advances in Computing Communications and Informatics Icacci 2017, 2017
    Local Field Potentials arising (LFP) from neural circuits are crucial to understand neural ensemble activity and can act as a link between molecular, cellular and circuit neuroscience. Additionally, mathematical estimations of LFPs allow the study of circuit functions and dysfunctions. In this study, we used mathematical reconstructions of LFP in rat cerebellum Crus IIa using spiking neuronal models and mass models based on lumped parameters to reconstruct the averaged ensemble activity. Comparing experimentally validated reconstructions of evoked LFPs using detailed multi-compartmental models, spiking neurons and lumped mass models suggest variations at the translational levels of biophysical mechanisms in granular layer. With the focus of reconnecting multiple information roles, our simulations studies indicate multi-compartmental detailed models allow estimations on the role of transmembrane currents, spiking neuron models suggest contributions of action potentials while mass models reveal averaged activity behaviour underlying Crus IIa evoked LFPs.
  • Mathematical modelling of cerebellar granular layer neurons and network activity: Information estimation, population behaviour and robotic abstractions
    Shyam Diwakar, Chaitanya Nutakki, Sandeep Bodda, Arathi Rajendran, Asha Vijayan, Bipin Nair
    Springer Indam Series, 2017
  • Categorizing imagined right and left motor imagery BCI tasks for low-cost robotic neuroprosthesis
    Sandeep Bodda, Harikrishnan Chandranpillai, Pooja Viswam, Swathy Krishna, Bipin Nair, Shyam Diwakar
    International Conference on Electrical Electronics and Optimization Techniques Iceeot 2016, 2016
    Focusing on low-cost articulation control for neuroprosthesis, electroencephalography (EEG)-based brain computer interfaces require rapid and reliable discrimination of EEG patterns associated with motor imagery generated via imagined or real movement. The objective of this study was to characterize EEG signals of two different motor imagery tasks used to control a robotic articulator. With one-sided hand movement imagination resulting in EEG changes located contra and ipsilateral areas, time-courses of two different imagery tasks were investigated via instantaneous band power changes. We compared the features extracted from the EEG patterns with standard machine learning algorithms. We report frequency-based categorization of visualized imagery more relevant than machine learning methods.
  • EEG-based assessment of image sequence-based user authentication in computer network security
    Priya Chellaiah, Sandeep Bodda, Rahul Lal, Clinton Madhu, Vaibhav Zamare, Bipin Nair, Shyam Diwakar, Priya Chellaiah, Krishnashree Achuthan
    International Conference on Electrical Electronics and Optimization Techniques Iceeot 2016, 2016
    User authentication is crucial in security systems. Although, there are many complex and secure passkey-based authentication mechanisms, majority of users prefer employing simple passwords that are viable to rubber-hose attacks. Image sequence based passwords were introduced to overcome some of the issues with textual passwords. The objective of this work was to evaluate cognitive and memory performance in image-based user authentication systems. Via EEG recordings during image sequence training task, we observed increased activity of α rhythms in F3 and FC5 channel bins and augmented levels of β rhythms in F3 and O1 channels, suggesting users personalized authentication more than in alpha-numeric password-based logins, linking potential roles of implicit and explicit sequence learning in improving human user authentication.
  • Computing LFP from biophysical models of neurons and neural microcircuits
    Sandeep Bodda, Harilal Parasuram, Bipin Nair, Shyam Diwakar
    2016 International Conference on Advances in Computing Communications and Informatics Icacci 2016, 2016
    Local Field Potentials (LFP) allow interpretations of patterns of information generated by neuronal populations. LFPs are Low frequency (<;300 Hz) population signals recorded with glass or metal electrodes and are known to be generated by complex spatiotemporal interactions of synaptic stimuli in combination with sink-source behavior in the circuit. Computational reconstruction of local field potentials allows to constrain detailed neuronal models and network microcircuits and study the function and dysfunctions via simulations. In this paper, we present a comparison of various methods and tools available for LFP computations in single neurons and populations of cells. We compare our LFPsim and ReConv methods to LFPy, VERTEX while mathematically computing local field potentials in single neurons and network models made with detailed multi-compartmental models and available through databases like ModelDB.
  • Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms
    Shyam Diwakar, Sandeep Bodda, Chaitanya Nutakki, Asha Vijayan, Krishnashree Achuthan, Bipin Nair
    International Joint Conference on Computational Intelligence, 2014
  • Neural control using EEG as a BCI technique for low cost prosthetic arms
    Shyam Diwakar, Sandeep Bodda, Chaitanya Nutakki, Asha Vijayan, Krishnashree Achuthan, Bipin Nair
    Ncta 2014 Proceedings of the International Conference on Neural Computation Theory and Applications, 2014

RECENT SCHOLAR PUBLICATIONS

  • Implementing and Deploying a Student Friendly GUI-based Platfrom for EEG signal processing
    R Alikkal, VH Akula, B Shankar, M Krishna, S Bodda, S Krishna, ...
    2025 International Conference on Robotics and Mechatronics (ICRM), 1-6 , 2025
    2025
  • Interconnections and global transitions among functional states encode activity-related dynamics as brain topology changes after yoga training
    S Bodda, S Diwakar
    Scientific Reports 15 (1), 16845 , 2025
    2025
    Citations: 4
  • Exploring EEG spectral and temporal dynamics underlying a hand grasp movement
    S Bodda, S Diwakar
    Plos One 17 (6) , 2022
    2022
    Citations: 16
  • Signal Processing in Yoga-Related Neural Circuits and Implications of Stretching and Sitting Asana on Brain Function
    D Kumar, AC Puthanveedu, K Mohan, LA Priya, A Rajeev, AC Harisudhan, ...
    Cybernetics, Cognition and Machine Learning Applications: Proceedings of … , 2021
    2021
    Citations: 2
  • Correlations of gait phase kinematics and cortical EEG: modelling human gait with data from sensors
    C Nutakki, S Bodda, S Diwakar
    Advances in Neural Signal Processing , 2020
    2020
    Citations: 5
  • Computational exploration of neural dynamics underlying music cues among trained and amateur subjects
    AK Santhosh, M Sangilirajan, N Nizar, R Radhamani, D Kumar, S Bodda, ...
    Procedia Computer Science 171, 1839-1847 , 2020
    2020
    Citations: 8
  • Computational analysis of EEG activity during stance and swing gait phases
    S Bodda, S Maya, MNE Potti, U Sohan, Y Bhuvaneshwari, R Mathiyoth, ...
    Procedia Computer Science 171, 1591-1597 , 2020
    2020
    Citations: 11
  • Experimental recording and assessing gait phases using mobile phone sensors and EEG
    A Balachandran, C Nutakki, S Bodda, B Nair, S Diwakar
    2018 International Conference on Advances in Computing, Communications and … , 2018
    2018
    Citations: 5
  • Activity: Information Estimation, Population
    S Diwakar, C Nutakki, S Bodda, A Rajendran, A Vijayan, B Nair
    Mathematical and Theoretical Neuroscience: Cell, Network and Data Analysis … , 2018
    2018
  • Modeling population network activity using lfpsim, spiking neurons and neural mass models
    S Bodda, RK Palathingal, V Sankar, B Nair, S Diwakar
    2017 International Conference on Advances in Computing, Communications and … , 2017
    2017
  • Cerebellum in Neurological Disorders: A Review on the Role of Inter-Connected Neural Circuits
    AG Rajendran, C Nutakki, H Sasidharakurup, S Bodda, B Nair, ...
    Journal of Neurology & Stroke 6 (2) , 2017
    2017
    Citations: 5
  • Mathematical Modelling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions
    S Diwakar, C Nutakki, S Bodda, R Arathi, A Vijayan, B Nair
    Mathematical and Theoretical Neuroscience 24, 61-85 , 2017
    2017
  • EEG-Based Assessment of Image Sequence-Based User Authentication in Computer Network Security
    P Chellaiah, S Bodda, RD Lal, C Madhu, V Zamare, B Nair, K Achuthan, ...
    Proceedings of International Conference on Electrical, Electronics and … , 2016
    2016
    Citations: 7
  • Categorizing Imagined Right and Left Motor Imagery BCI Tasks for Low-cost Robotic Neuroprosthesis
    S Bodda, H Chandranpillai, P Viswam, S Krishna, B Nair, S Diwakar
    Electrical, Electronics, and Optimization Techniques (ICEEOT), International … , 2016
    2016
    Citations: 7
  • Computing LFP from biophysical models of neurons and neural microcircuits
    S Bodda, H Parasuram, B Nair, S Diwakar
    2016 International Conference on Advances in Computing, Communications and … , 2016
    2016
    Citations: 2
  • Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms
    S Diwakar, S Bodda, C Nutakki, A Vijayan, K Achuthan, B Nair
    6th International Conference on Neural Computation Theory and Applications … , 2014
    2014
    Citations: 21

MOST CITED SCHOLAR PUBLICATIONS

  • Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms
    S Diwakar, S Bodda, C Nutakki, A Vijayan, K Achuthan, B Nair
    6th International Conference on Neural Computation Theory and Applications … , 2014
    2014
    Citations: 21
  • Exploring EEG spectral and temporal dynamics underlying a hand grasp movement
    S Bodda, S Diwakar
    Plos One 17 (6) , 2022
    2022
    Citations: 16
  • Computational analysis of EEG activity during stance and swing gait phases
    S Bodda, S Maya, MNE Potti, U Sohan, Y Bhuvaneshwari, R Mathiyoth, ...
    Procedia Computer Science 171, 1591-1597 , 2020
    2020
    Citations: 11
  • Computational exploration of neural dynamics underlying music cues among trained and amateur subjects
    AK Santhosh, M Sangilirajan, N Nizar, R Radhamani, D Kumar, S Bodda, ...
    Procedia Computer Science 171, 1839-1847 , 2020
    2020
    Citations: 8
  • EEG-Based Assessment of Image Sequence-Based User Authentication in Computer Network Security
    P Chellaiah, S Bodda, RD Lal, C Madhu, V Zamare, B Nair, K Achuthan, ...
    Proceedings of International Conference on Electrical, Electronics and … , 2016
    2016
    Citations: 7
  • Categorizing Imagined Right and Left Motor Imagery BCI Tasks for Low-cost Robotic Neuroprosthesis
    S Bodda, H Chandranpillai, P Viswam, S Krishna, B Nair, S Diwakar
    Electrical, Electronics, and Optimization Techniques (ICEEOT), International … , 2016
    2016
    Citations: 7
  • Correlations of gait phase kinematics and cortical EEG: modelling human gait with data from sensors
    C Nutakki, S Bodda, S Diwakar
    Advances in Neural Signal Processing , 2020
    2020
    Citations: 5
  • Experimental recording and assessing gait phases using mobile phone sensors and EEG
    A Balachandran, C Nutakki, S Bodda, B Nair, S Diwakar
    2018 International Conference on Advances in Computing, Communications and … , 2018
    2018
    Citations: 5
  • Cerebellum in Neurological Disorders: A Review on the Role of Inter-Connected Neural Circuits
    AG Rajendran, C Nutakki, H Sasidharakurup, S Bodda, B Nair, ...
    Journal of Neurology & Stroke 6 (2) , 2017
    2017
    Citations: 5
  • Interconnections and global transitions among functional states encode activity-related dynamics as brain topology changes after yoga training
    S Bodda, S Diwakar
    Scientific Reports 15 (1), 16845 , 2025
    2025
    Citations: 4
  • Signal Processing in Yoga-Related Neural Circuits and Implications of Stretching and Sitting Asana on Brain Function
    D Kumar, AC Puthanveedu, K Mohan, LA Priya, A Rajeev, AC Harisudhan, ...
    Cybernetics, Cognition and Machine Learning Applications: Proceedings of … , 2021
    2021
    Citations: 2
  • Computing LFP from biophysical models of neurons and neural microcircuits
    S Bodda, H Parasuram, B Nair, S Diwakar
    2016 International Conference on Advances in Computing, Communications and … , 2016
    2016
    Citations: 2
  • Implementing and Deploying a Student Friendly GUI-based Platfrom for EEG signal processing
    R Alikkal, VH Akula, B Shankar, M Krishna, S Bodda, S Krishna, ...
    2025 International Conference on Robotics and Mechatronics (ICRM), 1-6 , 2025
    2025
  • Activity: Information Estimation, Population
    S Diwakar, C Nutakki, S Bodda, A Rajendran, A Vijayan, B Nair
    Mathematical and Theoretical Neuroscience: Cell, Network and Data Analysis … , 2018
    2018
  • Modeling population network activity using lfpsim, spiking neurons and neural mass models
    S Bodda, RK Palathingal, V Sankar, B Nair, S Diwakar
    2017 International Conference on Advances in Computing, Communications and … , 2017
    2017
  • Mathematical Modelling of Cerebellar Granular Layer Neurons and Network Activity: Information Estimation, Population Behaviour and Robotic Abstractions
    S Diwakar, C Nutakki, S Bodda, R Arathi, A Vijayan, B Nair
    Mathematical and Theoretical Neuroscience 24, 61-85 , 2017
    2017