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I am a Pre Doctoral researcher working at Basque Centre on Cognition, Brain and Language, San Sebastian, Spain. I am registered in the Phd program in Cognitve Neuroscience at UPV,San Sebastian, Spain. I was a visiting fellow at Erame Hospital, ULB, Brussels for six months (Mar - Aug 2018). I also spent three months (Feb - May 2019) as Visiting student at Freie Universirty, Berlin, Germany. My research interests include Predictive processing across sensory modalities and Machine learning applications to Clinical neuroscience using MEG,EEG and fMRI.
Contextual information triggers predictions about the content (“what”) of environmental stimuli to update an internal generative model of the surrounding world. However, visual information dynamically changes across time, and temporal predictability (“when”) may influence the impact of internal predictions on visual processing. In this magnetoencep...
Emotion processing has been a focus of research in psychology and neuroscience for some decades. While the evoked neural markers in human brain activations in response to different emotions have been reported, the temporal dynamics of emotion processing has received less attention. Differences in processing speeds, that depend on emotion type, have...
Predictive processing has been proposed as a fundamental cognitive mechanism to account for how the brain interacts with the external environment via its sensory modalities. The brain processes external information about the content (i.e.,″what″)and timing (i.e.,″when″) of environmental stimuli to update an internal generative model of the world ar...
Worries preoccupy the working memory capacity in anxious individuals, thereby affecting their performance during tasks that require efficient attention regulation. According to the attentional control theory (ACT), trait anxiety affects the processing efficiency, i.e. the effort required for task performance, more than the accuracy of task performa...
Electroencephalogram (EEG) signals represent the neuronal activity of brain. These signals are recorded by placement of multiple electrodes over the scalp or from cortex of the brain under the skull. These signals have important applications in biomedical and clinical field but most applications fail to take benefit of all the data’s available from...
Squat is a popular yogic exercise or posture, most frequently used for quadriceps strengthening and rehabilitation. It is used for evaluation process or test by physiotherapists/clinicians/others associated with rehabilitation of athletes to strengthen their lower-body muscles and connective tissues after joint-related injury. It has been used exte...
Muscle coactivation is the activation of two or more muscles simultaneously around a joint. Coactivation of knee muscles especially quadriceps is considered to be an important phenomenon for the stabilization of patellofemoral joint. The purpose of this study was to investigate the Coactivation ratio of selected knee extensor muscles as measure of...
These datasets are exemplary segmented EEG time series recordings of ten epilepsy patients collected from Neurology & Sleep Centre, Hauz Khas, New Delhi. The data was acquired at sampling rate of 200 Hz using Grass Telefactor Comet AS40 Amplification System. During the acquisition, gold plated scalp EEG electrodes were placed according to 10-20 ele...
Simultaneous activation of muscles across a joint can be defined as muscle co-contraction. Its purpose is to augment the ligament function in maintenance of joint stability, provide resistance to rotation at a joint, and equalize the pressure distribution at the articular surface. The purpose of this study was to investigate the Co-contraction of s...
Coactivation is the activation of two or more things together. Simultaneous activity of the muscles acting around a joint is known as muscle coactivation. Coactivation of knee muscles is considered to be an important phenomenon for the stabilization of knee joint. The purpose of this paper was to investigate the influence of gender on Coactivation...
Over the past 25 years, Heart rate variability (HRV) has become a non-invasive research and clinical tool for indirectly carrying out investigation of both cardiac and autonomic system function in both healthy and diseased. It provides valuable information about a wide range of cardiovascular disorders, pulmonary diseases, neurological diseases, et...
holar Search 184,121,174 papers from all fields of science Sign In You are currently offline. Some features of the site may not work correctly. DOI:10.5120/15081-3520 Corpus ID: 30190135.oa Novel Notch Detection Algorithm for Detection of Dicrotic Notch in PPG Signals Sanjeev Nara, Manvinder Kaur, Kundan Lal Verma Published 2014 Computer Science P...
I have a 4 images with a square in the center, there are one condition in which are the squares have a difference of 45. So the square moves like 0 degee , 45 degree, 90 degree and 135 degree when i present these 4 pictures as a sequence. Second condition is when these squares moves randomly.
I shall be thankful if any one can suggest how to determine both the sequences and compare them. I mean i want a feature which can define the conditions. I will appreciate your valuable thoughts.
I have seen few papers which try to simulate the Visual Cortex using convolutional neural network (CNN). Is there any particular tool or library to simulate the Visual cortex or we can create it using the standard Deep learning libraries like Theano and Keras.
Respected EEG Experts,
I am totally new to EEG signal processing and I am starting this using EEGLAB. I have also gone through the manual of EEGLAB. My goal for using this is as follows:
1. I am having data set of continuous EEG of 3 mins in a block design of 30 seconds Active/Task and 30 seconds Base/Rest. Can you please suggest how I can show the difference in the active and base activities.
2. The experiment was performed by 3 groups having 10 10 subjects. How can I show the difference in groups using EEGLAB?
3. Is it possible to separate and calculate the alpha beta theta and delta frequencies and which is dominating in a particular channel using EEGLAB.
I apologize for asking various questions in the one question. Thanks:).
I have installed FSL 5.0 in my ubuntu 12.10. It's working fine when processed from GUI but when I try to run it using command line, it shows "command not found".
I know how to add toolboxes in Matlab for windows but I am unable to copy the scripts or toolboxes in the toolbox folder of Linux (Ubuntu 12.10).
The tbss is located in Bin directory written in light green colour .Please provide your valuable suggestions .
Much of our brain and mind activity focuses on the generation of predictions. The predictive coding framework offers a description of the implementation of such mechanisms both at the neural and cognitive level, offering an exciting possibility of furthering our understanding of the human nervous system, and its link to behaviour. The main body of evidence for predictive processing however emerges from the literature on basic visual and auditory processing. Empirical evidence for such a proposal in the language domain is scarce, he extent of anticipatory mechanisms is still debated, and the central role of prediction during language comprehension has often been challenged. In order for predictive processing to provide a unified description of human cognition and action, it must also account for the uniquely human ability of language. One obstacle to doing so is the difficulty in applying findings from basic perceptual research to a complex stimulus such as language. Up till now, studies of non-linguistic stimuli have focused on two dimensions of the predictive process separately: predicting what (mainly in the visual literature) and predicting when (auditory research). Given the temporally dynamic nature of language, apprehending both dimensions simultaneously might be the key to understanding predictive processing in this domain. The goal of the present project is thus to evaluate the correlates of predictive processing focusing on the relation between predictive coding (what) and predictive timing (when) for the first time. We will study these two mechanisms across modalities (visual and auditory) and across domains (basic perception and language processing) to deconstruct the mechanisms supporting predictive processing. By using state-of the art brain imaging (MEG) and analysis techniques (estimation of neural rhythms at the brain level) the present project will contribute to the understanding of how top-down preparatory activity may be implemented by oscillating neural populations in detail and how it affects perception in primary sensory regions. In addition, identifying such an oscillatory “signature” of linguistic anticipatory processing may be used to re-analyze and re-interpret previous classical paradigms within the field of psycholinguistics, and to design more focused studies in the future.