Skip to main content

Statistical Signal Processing in Neuroscience

  1. 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...

    Authors: Shalom Darmanjian and Jose Principe
    Citation: EURASIP Journal on Advances in Signal Processing 2010 2009:892461
  2. 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...

    Authors: Sady Antônio Santos Filho, Carlos Julio Tierra-Criollo, Ana Paula Souza, Marcos Antonio Silva Pinto, Maria Luiza Cunha Lima and Gilberto Mastrocola Manzano
    Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:534536
  3. 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....

    Authors: Derong Liu, Zhongyu Pang and Zhuo Wang
    Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:638534
  4. 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...

    Authors: Zhenghui Hu, Xiaohu Zhao, Huafeng Liu and Pengcheng Shi
    Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:215409
  5. 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 ...

    Authors: Aurel A. Lazar and Eftychios A. Pnevmatikakis
    Citation: EURASIP Journal on Advances in Signal Processing 2009 2009:682930