Spike removal through multiscale wavelet and entropy analysis of ocular motor noise: A case study in patients with cerebellar disease

What can entropy metrics tell us about the characteristics of ocular fixation trajectories?

In this study, we provide a detailed analysis of entropy measures calculated for fixation eye movement trajectories from the three different datasets. We employed six key metrics (Fuzzy, Increment, Sample, Gridded Distribution, Phase, and Spectral Entropies). We calculate these six metrics on three sets of fixations: (1) fixations from the GazeCom dataset, (2) fixations from what we refer to as the "Lund" dataset, and (3) fixations from our own research laboratory ("OK Lab" dataset). For each entropy measure, for each dataset, we closely examined the 36 fixations with the highest entropy and the 36 fixations with the lowest entropy. From this, it was clear that the nature of the information from our entropy metrics depended on which dataset was evaluated. These entropy metrics found various types of misclassified fixations in the GazeCom dataset. Two entropy metrics also detected fixation with substantial linear drift. For the Lund dataset, the only finding was that low spectral entropy was associated with what we call "bumpy" fixations. These are fixations with low-frequency oscillations. For the OK Lab dataset, three entropies found fixations with high-frequency noise which probably represent ocular microtremor. In this dataset, one entropy found fixations with linear drift. The between-dataset results are discussed in terms of the number of fixations in each dataset, the different eye movement stimuli employed, and the method of eye movement classification.

Gravitational models explain shifts on human visual attention

Visual attention refers to the human brain’s ability to select relevant sensory information for preferential processing, improving performance in visual and cognitive tasks. It proceeds in two phases. One in which visual feature maps are acquired and processed in parallel. Another where the information from these maps is merged in order to select a single location to be attended for further and more complex computations and reasoning. Its computational description is challenging, especially if the temporal dynamics of the process are taken into account. Numerous methods to estimate saliency have been proposed in the last 3 decades. They achieve almost perfect performance in estimating saliency at the pixel level, but the way they generate shifts in visual attention fully depends on winner-take-all (WTA) circuitry. WTA is implemented by the biological hardware in order to select a location with maximum saliency, towards which to direct overt attention. In this paper we propose a gravitational model to describe the attentional shifts. Every single feature acts as an attractor and the shifts are the result of the joint effects of the attractors. In the current framework, the assumption of a single, centralized saliency map is no longer necessary, though still plausible. Quantitative results on two large image datasets show that this model predicts shifts more accurately than winner-take-all.

A Novel Method for Electrophysiological Analysis of EMG Signals Using MesaClip

In electrophysiology, many methods have been proposed for the analysis of action potential firing frequencies. The aim of this study was to present an algorithm developed for a continuous wavelet transform that enables the filtering out of frequencies contributing to the shapes of action potentials (spikes), whilst retaining the frequencies that encode the periodicity of spike trains. The continuous wavelet transform allows us to decompose a signal into its constituent frequencies. A signal with a single event, such as a spike, is composed of frequencies that characterize the shape of the spike. A signal with two spikes will also be composed of frequencies characterizing the shape of the action potential, but in addition will include a substantial portion of its power at the frequency corresponding to the time-difference between the two spikes. This is achieved by clipping peaks from the wavelet amplitudes that are narrower than a given minimum number of phase cycles. We present some application examples in both synthetic signals and electrophysiological recordings. This new approach can provide a major new analytical tool for analysis of electrophysiological signals.

Spatial ranking strategy and enhanced peripheral vision discrimination optimize performance and efficiency of visual sequential search

Visual sequential search might use a peripheral spatial ranking of the scene to put the next target of the sequence in the correct order. This strategy, indeed, might enhance the discriminative capacity of the human peripheral vision and spare neural resources associated with foveation. However, it is not known how exactly the peripheral vision sustains sequential search and whether the sparing of neural resources has a cost in terms of performance. To elucidate these issues, we compared strategy and performance during an alpha-numeric sequential task where peripheral vision was modulated in three different conditions: normal, blurred, or obscured. If spatial ranking is applied to increase the peripheral discrimination, its use as a strategy in visual sequencing should differ according to the degree of discriminative information that can be obtained from the periphery. Moreover, if this strategy spares neural resources without impairing the performance, its use should be associated with better performance. We found that spatial ranking was applied when peripheral vision was fully available, reducing the number and time of explorative fixations. When the periphery was obscured, explorative fixations were numerous and sparse; when the periphery was blurred, explorative fixations were longer and often located close to the items. Performance was significantly improved by this strategy. Our results demonstrated that spatial ranking is an efficient strategy adopted by the brain in visual sequencing to highlight peripheral detection and discrimination; it reduces the neural cost by avoiding unnecessary foveations, and promotes sequential search by facilitating the onset of a new saccade.

Evaluating the influence of motor control on selective attention through a stochastic model: the paradigm of motor control dysfunction in cerebellar patient

Attention allows us to selectively process the vast amount of information with which we are confronted, prioritizing some aspects of information and ignoring others by focusing on a certain location or aspect of the visual scene. Selective attention is guided by two cognitive mechanisms: saliency of the image (bottom up) and endogenous mechanisms (top down). These two mechanisms interact to direct attention and plan eye movements; then, the movement profile is sent to the motor system, which must constantly update the command needed to produce the desired eye movement. A new approach is described here to study how the eye motor control could influence this selection mechanism in clinical behavior: two groups of patients (SCA2 and late onset cerebellar ataxia LOCA) with well-known problems of motor control were studied; patients performed a cognitively demanding task; the results were compared to a stochastic model based on Monte Carlo simulations and a group of healthy subjects. The analytical procedure evaluated some energy functions for understanding the process. The implemented model suggested that patients performed an optimal visual search, reducing intrinsic noise sources. Our findings theorize a strict correlation between the "optimal motor system" and the "optimal stimulus encoders."

Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals

To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient (ρ), mean of coherence function (C), and rate of absolute error (RAE). All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with ρ>0.85, C>0.8, and RAE<0.5. These values were significantly better than the performance of LMS adaptive filter (ρ<0.85, C<0.6, and RAE>1).