Test-Retest Reliability of Sensory Evoked Potentials in Low-Functioning Autism: A Case Report

Sensory abnormalities are common in individuals with autism spectrum disorders (ASD) but are often difficult to assess using standard behavioral methods. Evoked potentials provide objective, non-invasive electrophysiological measures of neural sensory processing that could be useful for clinical and investigative studies of individuals with low-functioning autism who are unable to perform behavioral testing. Despite increased use, the reliability of sensory evoked potentials has not been established for individuals with low-functioning autism. Establishing reliability is important for validating the utility of sensory evoked potentials. In this study, we explored the feasibility of assessing the test-retest reliability of sensory evoked potentials using repeat recordings, acquired over 2.5- and 6-month intervals, from a minimally verbal adult with low-functioning autism. Repeat auditory and visual evoked potential recordings showed high test-retest reliability, with cross-correlation coefficients ≥ 0.80. This case demonstrates the feasibility of establishing test-retest reliability for individuals with low-functioning autism and supports the utility of sensory evoked potentials in clinical and investigative ASD studies.

Multidimensional clinical data denoising via Bayesian CP factorization

CANDECOMP/PARAFAC(CP) tensor factorization is an efficient technique for incomplete tensor-data processing through capturing the multilinear latent factors. Based on the incorporate a sparsity-inducing prior over multiple latent factors and appropriate hyper-priors over all hyper-parameters, a Bayesian-based hierarchical probabilistic CP factorization model could be formed. By this way, the rank of the incomplete tensor can be determined automatically. In this paper, we explored the tensor completion method in processing incomplete multidimensional electroencephalogram(EEG) and magnetic resonance imaging(MRI) clinical data. The empirical results indicated that the Bayesian CP tensor factorization of incomplete data method can effectively recover EEG signal with missing data and denoised the noisy MRI data.

A classification-based method to estimate event-related potentials from single trial EEG

A novel method based on machine learning is developed to estimate event-related potentials from single trial electroencephalography. This paper builds a basic framework using classification and an optimization model based on this framework for estimating event-related potentials. Then the SingleTrialEM algorithm is derived by introducing a logistic regression model, which could be obtained by training before SingleTrialEM is used, to instantiate the optimization model. The simulation tests demonstrate that the proposed method is correct and solid. The advantage of this method is verified by the comparison between this method and the Woody filter in simulation tests. Also, the cognitive test results are consistent with the conclusions of cognitive science.

Survey of visual sentiment prediction for social media analysis

Recent years have witnessed a rapid spread of multi-modality microblogs like Twitter and Sina Weibo composed of image, text and emoticon. Visual sentiment prediction of such microblog based social media has recently attracted ever-increasing research focus with broad application prospect. In this paper, we give a systematic review of the recent advances and cutting-edge techniques for visual senti- ment analysis. To this end, in this paper we review the most recent works in this topic, in which detailed comparison as well as experimental evaluation are given over the cuttingedge methods. We further reveal and discuss the future trends and potential directions for visual sentiment prediction.

Learning Robotic Hand-eye Coordination Through a Developmental Constraint Driven Approach

The skill of robotic hand-eye coordination not only helps robots to deal with real time environment,but also afects the fundamental framework of robotic cognition.A number of approaches have been developed in the literature for construction of the robotic hand-eye coordination.However,several important features within infant developmental procedure have not been introduced into such approaches.This paper proposes a new method for robotic hand-eye coordination by imitating the developmental progress of human infants.The work employs a brain-like neural network system inspired by infant brain structure to learn hand-eye coordination,and adopts a developmental mechanism from psychology to drive the robot.The entire learning procedure is driven by developmental constraint： The robot starts to act under fully constrained conditions,when the robot learning system becomes stable,a new constraint is assigned to the robot.After that,the robot needs to act with this new condition again.When all the contained conditions have been overcome,the robot is able to obtain hand-eye coordination ability.The work is supported by experimental evaluation,which shows that the new approach is able to drive the robot to learn autonomously,and make the robot also exhibit developmental progress similar to human infants.

From Biological Consciousness to Machine Consciousness:An Approach to Make Smarter Machines

Consciousness research has been of great concern to philosophers, psychologists and neuroscientists in recent years. At the same time, consciousness has also attracted more and more interest of artificial intelligence （AI） researchers. In order to make more intelligent machines, many computing models of machine consciousness have been presented. Furthermore, self-consciousness has relevance to the level of intelligent functions. Hence, it is necessary to study self-consciousness in AI. This thesis, starting from biological consciousness, discusses some viewpoints of machine consciousness. Based on the discussions, we present a way to emulate self-consciousness and test this method via simulation experiments. Our results indicate that self-consciousness, which belongs to organisms, can he imitated by machines.

A novel single-trial event-related potential estimation method based on compressed sensing

Cognitive functions are often studied using eventrelated potentials（ERPs）that are usually estimated by an averaging algorithm.Clearly,estimation of single-trial ERPs can provide researchers with many more details of cognitive activity than the averaging algorithm.A novel method to estimate single-trial ERPs is proposed in this paper.This method includes two key ideas.First,singular value decomposition was used to construct a matrix,which mapped singletrial electroencephalographic recordings（EEG）into a low-dimensional vector that contained little information from the spontaneous EEG.Second,we used the theory of compressed sensing to build a procedure to restore single-trial ERPs from this low-dimensional vector.ERPs are sparse or approximately sparse in the frequency domain.This fact allowed us to use the theory of compressed sensing.We verified this method in simulated and real data.Our method and dVCA（differentially variable component analysis）,another method of single-trial ERPs estimation,were both used to estimate single-trial ERPs from the same simulated data.Results demonstrated that our method significantly outperforms dVCA under various conditions of signal-to-noise ratio.Moreover,the single-trial ERPs estimated from the real data by our method are statistically consistent with the theories of cognitive science.

Topic-aware pivot language approach for statistical machine translation

The pivot language approach for statistical machine translation(SMT) is a good method to break the resource bottleneck for certain language pairs. However, in the implementation of conventional approaches, pivotside context information is far from fully utilized, resulting in erroneous estimations of translation probabilities. In this study, we propose two topic-aware pivot language approaches to use different levels of pivot-side context. The first method takes advantage of document-level context by assuming that the bridged phrase pairs should be similar in the document-level topic distributions. The second method focuses on the effect of local context. Central to this approach are that the phrase sense can be reflected by local context in the form of probabilistic topics, and that bridged phrase pairs should be compatible in the latent sense distributions. Then, we build an interpolated model bringing the above methods together to further enhance the system performance. Experimental results on French-Spanish and French-German translations using English as the pivot language demonstrate the effectiveness of topic-based context in pivot-based SMT.

Characterizing the human-robot haptic dyad in robot therapy of stroke survivors

Purpose–The working hypothesis,on which this paper is built,is that it is advantageous to look at protocols of robot rehabilitation in the general context of human-robot interaction in haptic dyads.The purpose of this paper is to propose a new method to detect and evaluate an index of active participation(AC index),underlying the performance of robot-assisted movements.This is important for avoiding the slacking phenomenon that affects robot therapy.Design/methodology/approach–The evaluation of the AC index is based on a novel technique of assistance which does not use constant or elastic forces but trains of small force impulses,with amplitude adapted to the level of impairment and a frequency of 2 Hz,which is suggested by recent results in the field of intermittent motor control.A preliminary feasibility test of the proposed method was carried out during a haptic reaching task in the absence of visual feedback,for a group of five stroke patients and an equal group of healthy subjects.Findings–The AC index appears to be stable and sensitive to training in both populations of subjects.Originality/value–The main original element of this study is the proposal of the new AC index of voluntary control associated with the new method of pulsed haptic interaction.