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Rajanikant Panda

Researcher at University of Liège

Publications -  55
Citations -  907

Rajanikant Panda is an academic researcher from University of Liège. The author has contributed to research in topics: Resting state fMRI & Consciousness. The author has an hindex of 13, co-authored 42 publications receiving 535 citations. Previous affiliations of Rajanikant Panda include Academy of Technology & National Institute of Mental Health and Neurosciences.

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Proceedings ArticleDOI

Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction

TL;DR: SVM (support vector machine) based classifier was employed to detect epileptic seizure activity from background electro encephalographs (EEGs) and shows the promising classification accuracy of nearly 91.2% in detection of abnormal from normal EEG signals.
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Recovery of resting brain connectivity ensuing mild traumatic brain injury

TL;DR: Hyper connectivity of several networks supported normal recovery in the first 6 months and it remains to be seen in future studies whether this can predict an early and efficient recovery of brain function.
Journal ArticleDOI

Role of altered cerebello-thalamo-cortical network in the neurobiology of essential tremor

TL;DR: Seed-to-voxel resting state connectivity analysis revealed significant alterations in the cerebello-thalamo-cortical network in patients with ET, which further support the previous evidence of cerebellar pathology in ET.
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Temporal Dynamics of the Default Mode Network Characterize Meditation-Induced Alterations in Consciousness.

TL;DR: It is found that the alteration in the duration of the DMN microstate when meditators entered the meditative state correlated negatively with their years of meditation experience, reflecting a trait effect of meditation, highlighting its role in producing durable changes in temporal dynamics of theDMN.
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Decreased integration of EEG source-space networks in disorders of consciousness.

TL;DR: High-density electroencephalography data showed that networks in DOC patients are characterized by impaired global information processing and increased local information processing (network segregation) as compared to controls and the large-scale functional brain networks had integration decreasing with lower level of consciousness.