R
Rohit Bose
Researcher at University of Pittsburgh
Publications - 54
Citations - 460
Rohit Bose is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Feature extraction & Electroencephalography. The author has an hindex of 9, co-authored 47 publications receiving 241 citations. Previous affiliations of Rohit Bose include Calcutta Institute of Engineering and Management & National University of Singapore.
Papers
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Journal ArticleDOI
Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non‐focal electroencephalogram signals
TL;DR: It has been observed that the proposed MFDFA aided feature extraction method delivers quite commensurable and even better results in discriminating F and NF EEG signals, compared with the existing methods studied on the similar database.
Journal ArticleDOI
Detection of epileptic seizure and seizure-free EEG signals employing generalised S-transform
TL;DR: A novel technique for classification of electroencephalogram (EEG) signals has been presented employing generalised Stockwell ( S )-transform technique, where both EEG signals of healthy and inter-ictal zone are considered to be in seizure-free class.
Journal ArticleDOI
Detection of epileptic seizure employing a novel set of features extracted from multifractal spectrum of electroencephalogram signals
TL;DR: It is observed that 100% classification accuracy is obtained for three problems which validate the efficacy of the proposed model for computer-aided diagnosis of epilepsy.
Journal ArticleDOI
Decoding of Pain Perception using EEG Signals for a Real-Time Reflex System in Prostheses: A Case Study.
Zied Tayeb,Rohit Bose,Rohit Bose,Andrei Dragomir,Andrei Dragomir,Luke Osborn,Luke Osborn,Nitish V. Thakor,Nitish V. Thakor,Gordon Cheng +9 more
TL;DR: This work presents a novel approach and a first attempt to analyze and classify neural activity when restoring sensory perception to amputees, which could chart a route ahead for designing a real-time pain reaction system in upper-limb prostheses.
Proceedings ArticleDOI
Classification of lower limb motor imagery using K Nearest Neighbor and Naïve-Bayesian classifier
TL;DR: This study focuses on finding the best feature extraction technique and classifier for classification of right and left lower limb motor imagery movement from brain signals by k-Nearest Neighbor (kNN) and Naïve-Bayesian classifier.