Pavan Ramkumar

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In the absence of external stimuli, human hemodynamic brain activity displays slow intrinsic variations. To find out whether such fluctuations would be altered by persistent pain, we asked 10 patients with unrelenting chronic pain of different etiologies and 10 sex- and age-matched control subjects to rest with eyes open during 3-T functional MRI.(More)
Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more "interesting" sources than with(More)
This report summarizes the modeling challenge held in conjunction with the International Conference on Artificial Neural Networks (ICANN) 2011 and sponsored by the PASCAL2 Challenge Programme. The challenge aimed at promoting awareness of the task “mind reading” or “brain decoding” based on magnetoencephalography (MEG) data. For neuroscientists, the task(More)
Current knowledge about the precise timing of visual input to the cortex relies largely on spike timings in monkeys and evoked-response latencies in humans. However, quantifying the activation onset does not unambiguously describe the timing of stimulus-feature-specific information processing. Here, we investigated the information content of the early human(More)
In this paper, we explore visually evoked potentials (VEPs) as a potential tool for biometric identification. Using a clinical stimulation paradigm, single channel pattern onset VEPs are recorded from raw EEG from 10 healthy male subjects aged between 20 and 24. Following this, two feature extraction techniques are employed to characterize the signals.(More)
Independent component analysis (ICA) is increasingly used to analyze patterns of spontaneous activity in brain imaging. However, there are hardly any methods for answering the fundamental question: are the obtained components statistically significant? Most methods considering the significance of components either consider group-differences or use arbitrary(More)
Rhythmic brain activity, measured by magnetoencephalography (MEG), is modulated during stimulation and task performance. Here, we introduce an oscillatory response function (ORF) to predict the dynamic suppression-rebound modulation of brain rhythms during a stimulus sequence. We derived a class of parametric models for the ORF in a generalized convolution(More)
When we search for visual objects, the features of those objects bias our attention across the visual landscape (feature-based attention). The brain uses these top-down cues to select eye movement targets (spatial selection). The frontal eye field (FEF) is a prefrontal brain region implicated in selecting eye movements and is thought to reflect(More)
Neem is one of the most widely researched tropical tree, with almost all it's parts being put for a variety of uses. In the present study, the antibacterial effect of Neem mouthwash against salivary levels of streptococcus mutans and lactobacillus has been tested over a period of 2 months. Also it's effect in reversing incipient carious lesions was(More)
Independent component analysis (ICA) of electroencephalographic (EEG) and magnetoencephalographic (MEG) data is usually performed over the temporal dimension: each channel is one row of the data matrix, and a linear transformation maximizing the independence of component time courses is sought. In functional magnetic resonance imaging (fMRI), by contrast,(More)