no code implementations • 13 May 2017 • Shre Kumar Chatterjee, Saptarshi Das, Koushik Maharatna, Elisa Masi, Luisa Santopolo, Ilaria Colzi, Stefano Mancuso, Andrea Vitaletti
Plants monitor their surrounding environment and control their physiological functions by producing an electrical response.
no code implementations • 29 Nov 2016 • Wasifa Jamal, Saptarshi Das, Koushik Maharatna, Fabio Apicella, Georgia Chronaki, Federico Sicca, David Cohen, Filippo Muratori
However, here we report a similar observation of quasi-stable phase synchronised states in multichannel EEG.
no code implementations • 29 Nov 2016 • Shre Kumar Chatterjee, Saptarshi Das, Koushik Maharatna, Elisa Masi, Luisa Santopolo, Stefano Mancuso, Andrea Vitaletti
Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes.
no code implementations • 17 Jul 2015 • Sanmitra Ghosh, Srinandan Dasmahapatra, Koushik Maharatna
Approximate Bayesian computation (ABC) using a sequential Monte Carlo method provides a comprehensive platform for parameter estimation, model selection and sensitivity analysis in differential equations.
no code implementations • 20 Oct 2014 • Valentina Bono, Wasifa Jamal, Saptarshi Das, Koushik Maharatna
In order to reduce the muscle artifacts in multi-channel pervasive Electroencephalogram (EEG) signals, we here propose and compare two hybrid algorithms by combining the concept of wavelet packet transform (WPT), empirical mode decomposition (EMD) and Independent Component Analysis (ICA).
no code implementations • 20 Oct 2014 • Wasifa Jamal, Saptarshi Das, Ioana-Anastasia Oprescu, Koushik Maharatna
This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals.
no code implementations • 20 Oct 2014 • Wasifa Jamal, Saptarshi Das, Ioana-Anastasia Oprescu, Koushik Maharatna, Fabio Apicella, Federico Sicca
The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects.