Carlos A. Loza

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We introduce a novel variation on the well-known Matching Pursuit (MP) algorithm. In particular, the sparse approximation problem is solved in a greedy scheme using estimated higher-order statistics as similarity measures instead of the somehow limited second-order statistics that perform optimally only under Gaussian assumptions. This is conveyed via the(More)
We introduce a novel, transient model for the electroencephalogram (EEG) as the noisy addition of linear filters responding to trains of delta functions. We set the synthesis part as a parameter-tuning problem and obtain synthetic EEG-like data that visually resembles brain activity in the time and frequency domains. For the analysis counterpart, we use(More)
Electrocorticogram (ECoG) based Brain-Computer Interfaces (BCI) provide finer spatial resolution and improved signal-to-noise ratio than its noninvasive counterpart, Electroencephalogram (EEG). This remarkable feature allows for processing in higher spectral bands in order to elucidate more spatially localized encoding mechanisms. We propose an automatic,(More)
We introduce a method that incorporates robustness to one of the main building blocks of sparse modeling: dictionary learning. Particularly, we exploit correntropy to compute the principal components in cases where outliers might be detrimental without proper care. This is further added to one of the most utilized dictionary learning tools: K-SVD; the(More)
We propose a novel interpretation of single channel Electroencephalogram (EEG) traces based on the transient nature of encoded processes in the brain. In particular, the proposed framework models EEG as the output of the noisy addition of temporal, reoccurring, transient patterns known as phasic events. This is not only neurophysiologically sound, but it(More)
The Electroencephalogram (EEG) is a non-invasive technique used in the medical field to record and analyze brain activity. In particular, Brain Machine Interfaces (BMI) create this bridge between brain signals and the external world through prosthesis, visual interfaces and other physical devices. This paper investigates the relation between particular hand(More)
The auditory cortex in the brain does effortlessly a better job of extracting information from the acoustic world than our current generation of signal processing algorithms. The proposed architecture, Hierarchical Linear Dynamical System (HLDS), is based on Kalman filters with hierarchically coupled state models that stabilize the input dynamics and(More)
Neuromodulations are an important component of extracellular electrical potentials (EEP), such as the Electroencephalogram (EEG), Electrocorticogram (ECoG) and Local Field Potentials (LFP). This spatially temporal organized multi-frequency transient (phasic) activity reflects the multiscale spatiotemporal synchronization of neuronal populations in response(More)
We propose a novel interpretation of local field potentials (LFP) based on a marked point process (MPP) framework that models relevant neuromodulations as shifted weighted versions of prototypical temporal patterns. Particularly, the MPP samples are categorized according to the well known oscillatory rhythms of the brain in an effort to elucidate spectrally(More)
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