Role Of Phase Information Research Articles

A novel cepstral-based technique for automatic cognitive load estimation

The development of a reliable algorithm with low computational requirement is a challenge in cognitive load estimation. To address this challenge, this paper presented a view towards the novel application of cepstrum analysis in workload estimation. The proposed method used minimum number of physiological signals. It integrated amplitude and phase information of electrocardiogram (ECG) signals while reducing feature sample size. A set of cepstral-based ECG features, such as dynamic cepstral warping (DCW), differential energy density, and differential statistical features was proposed for designing a new cognitive load estimation system. The features were processed by principal component analysis and support vector machine in order to discriminate different levels of an arithmetic task. The discriminating capability of the method was evaluated using the ECG recording of 22 healthy subjects. The proposed algorithm achieved high average accuracy of 92.27% and 90.34% for the workload levels determined by the variation in the digit numbers and in the number of carry operations, respectively. In the case of combination both variables, the average accuracy of 90.48% was obtained. Furthermore, comparing between complex and real cepstral measures revealed better performance of the complex measures. These findings indicated the role of phase information in the ECG-based cognitive load estimation. Integrating dynamic and static characteristics of the cepstral coefficients in a multivariate approach improved the performance of the system. It performed significantly better than popular ECG features. The proposed approach provided a trade-off between performance and computational complexity of the estimation system.

On the role of spatial phase and phase correlation in vision, illusion, and cognition.

Numerous findings indicate that spatial phase bears an important cognitive information. Distortion of phase affects topology of edge structures and makes images unrecognizable. In turn, appropriately phase-structured patterns give rise to various illusions of virtual image content and apparent motion. Despite a large body of phenomenological evidence not much is known yet about the role of phase information in neural mechanisms of visual perception and cognition. Here, we are concerned with analysis of the role of spatial phase in computational and biological vision, emergence of visual illusions and pattern recognition. We hypothesize that fundamental importance of phase information for invariant retrieval of structural image features and motion detection promoted development of phase-based mechanisms of neural image processing in course of evolution of biological vision. Using an extension of Fourier phase correlation technique, we show that the core functions of visual system such as motion detection and pattern recognition can be facilitated by the same basic mechanism. Our analysis suggests that emergence of visual illusions can be attributed to presence of coherently phase-shifted repetitive patterns as well as the effects of acuity compensation by saccadic eye movements. We speculate that biological vision relies on perceptual mechanisms effectively similar to phase correlation, and predict neural features of visual pattern (dis)similarity that can be used for experimental validation of our hypothesis of “cognition by phase correlation.”

On the role of phase information in the perception of 3D shape from texture

On the role of phase information in the perception of 3D shape from texture

Role of phase information and eye pursuit in the detection of moving objects in noise.

As part of an ongoing study that uses objective image quality measures to optimize medical imaging x-ray fluoroscopy, we investigated two basic features of the detection of moving cylinders that mimic arteries, catheters, and guide wires. First, we compared detection with and without a phase cue consisting of a nearby alternating light and dark square. Depending on object size and velocity, phase cuing improved detection from 1% to 15% and gave an average of 6%, an effect much smaller than the 38% predicted from a Monte Carlo simulation of the ideal observer. Evidently, humans were limited in their ability to incorporate knowledge of the phase cue. Second, we evaluated the effect of eye pursuit of a fixation point that moved with the target. In general, motion at the highest velocity degraded (74%) and enhanced (68%) detection of small and large objects, respectively. With eye pursuit, both effects were substantially reduced in a manner consistent with a reduced retinal velocity. Our data compared favorably with a human observer model that included a spatiotemporal contrast sensitivity response and smooth-pursuit eye movements with a gain of 0.8. These mechanisms of perception are thought to be present in coronary artery x-ray fluoroscopy imaging, where phase information is available from the moving heart and where motion markers are available from x-ray opaque markers incorporated in thin catheters and guide wires.

On the role of phase information in conditional sampling

Conditional sampling techniques have been studied in order to better understand the phase relationship between the signal used to detect the events and the sampled data signal. It is shown that whenever there is a mean time delay between these two signals or whenever they are obtained from two different spatial locations, a random phase will exist between them if the signals are obtained from a random environment. This randomness is not accounted for in the usual definitions of conditional sampling, and it can cause a serious degradation of the conditionally averaged results. A corrective method is discussed and illustrated with examples from a turbulent boundary layer.