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We present a preliminary quantitative study aimed at developing an optimal standard protocol for automatic classification of specific affective states as related to human- computer interactions. This goal is mainly achieved by comparing standard psychological test-reports to quantitative measures derived from simultaneous non-invasive acquisition of psychophysiological signals of interest, namely...
A visual display of stripes was used to examine cardiovagal response to motion sickness. Heart rate variability (HRV) was investigated using dynamic methods to discern instantaneous fluctuations in reaction to stimulus and perception-based events. A novel point process adaptive recursive algorithm was applied to the R-R series to compute instantaneous heart rate, HRV, and high frequency (HF) power...
Tracking the autonomic control and respiratory sinus arrhythmia (RSA) from electrocardiogram and respiratory measurements is an important problem in cardiovascular control. We propose a point process adaptive filter algorithm based on an inverse Gaussian model to track heart beat intervals that incorporates respiratory measurements as a covariate and provides an analytic form for computing a dynamic...
In this paper, we compare and validate different probabilistic models of human heart beat intervals for assessment of the electrocardiogram data recorded with varying conditions in posture and pharmacological autonomic blockade. The models are validated using the adaptive point process filtering paradigm and Kolmogorov-Smirnov test. The inverse Gaussian model was found to achieve the overall best...
Developing optimal strategies for constructing and testing decoding algorithms is an important question in computational neuroscience, In this field, decoding algorithms are mathematical methods that model ensemble neural spiking activity as they dynamically represent a biological signal. We present a recursive decoding algorithm based on a Bayesian point process model of individual neuron spiking...
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