Statistical Signal Processing in Neuroscience
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Citation: EURASIP Journal on Advances in Signal Processing 2010 2009:105086
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Spatial-Temporal Clustering of Neural Data Using Linked-Mixtures of Hidden Markov Models
This paper builds upon the previous Brain Machine Interface (BMI) signal processing models that require apriori knowledge about the patient's arm kinematics. Specifically, we propose an unsupervised hierarchic...
Citation: EURASIP Journal on Advances in Signal Processing 2010 2009:892461 -
Magnitude Squared of Coherence to Detect Imaginary Movement
This work investigates the Magnitude Squared of Coherence (MSC) for detection of Event Related Potentials (ERPs) related to left-hand index finger movement. Initially, ERP presence was examined in different br...
Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:534536 -
A Method for Visualizing Independent Spatio-Temporal Patterns of Brain Activity
Evoked and coordinated brain signals often exhibit distinct, individualized spatial and temporal characteristics, such as amplitude and phase couplings across and within spatial channels. In the study of these...
Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:948961 -
Epileptic Seizure Prediction by a System of Particle Filter Associated with a Neural Network
None of the current epileptic seizure prediction methods can widely be accepted, due to their poor consistency in performance. In this work, we have developed a novel approach to analyze intracranial EEG data....
Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:638534 -
Nonlinear Analysis of the BOLD Signal
The linearized filtering approach to the hemodynamic system is limited in capturing the inherent nonlinearities of physiological systems. The nonlinear estimation method therefore should be thought of as a nat...
Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:215409 -
Reconstruction of Sensory Stimuli Encoded with Integrate-and-Fire Neurons with Random Thresholds
We present a general approach to the reconstruction of sensory stimuli encoded with leaky integrate-and-fire neurons with random thresholds. The stimuli are modeled as elements of a Reproducing Kernel Hilbert ...
Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:682930