Visual short term memory

OBJECTIVE: To establish reference values for the computerized cognitive test and evaluate cognitive function improvements across different age groups, we introduce the computerized Cognitive Function Test program (eCFT), specifically designed for children. We aimed to establish eCFT reference values and assess cognitive function improvements across different age groups. METHODS: We included children aged 3-6 years with confirmed normal cognition based on the Korean Developmental Screening Test for Infants and Children and Kaufman Assessment Battery for Children-II...

Myocardial infarction (MI) is a common cardiovascular disease caused by the blockages of coronary arteries. The visual inspection of electrocardiogram (ECG) is the main diagnosis pattern, while it is taxing and time-consuming. Motivated from state refinement module for long short term memory (SRM-LSTM), we proposed two improved state refinement frameworks based on LSTM and gated recurrent unit (GRU) called ISRM-LSTM and ISRM-GRU. Both are capable of adaptively refining current states of sample points in ECG with a message passing mechanism than existing LSTM...

BACKGROUND: Being able to differentiate mild cognitive impairment (MCI) patients who would eventually convert (MCIc) to Alzheimer's disease (AD) from those who would not (MCInc) is a key challenge for prognosis. OBJECTIVE: This study aimed to investigate the ability of sulcal morphometry to predict MCI progression to AD, dedicating special attention to an accurate identification of sulci. METHODS: Twenty-five AD patients, thirty-seven MCI and twenty-five healthy controls (HC) underwent a brain-MR protocol (1...

In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations...

Objective: The Visual Short-Term Memory Binding (VSTMB) Test is a useful tool in the assessment of Alzheimer's disease (AD). Research has suggested that short-term memory binding is insensitive to the sociocultural characteristics of the assessed individuals. Such earlier studies addressed this influence by considering years of education. The current study aims to determine the influence of sociocultural factors via a measure of Socioeconomic Status (SES) which provides a more holistic approach to these common confounders...

Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum...

Different tests measure text comprehension, including the cloze gap-filling test, often used for language learning. Different studies hypothesized cognitive strategies in this type of test and their relationship with working memory and performance. However, no study investigated the cloze test, working memory, and possible cognitive strategies, while performing the test. Therefore, this study aimed to identify cognitive visual strategies in the cloze test by applying an unsupervised algorithm and to analyze the relationship between these strategies with working memory and performance in the cloze test...

1D nanowire networks, sharing similarities of structure, information transfer, and computation with biological neural networks, have emerged as a promising platform for neuromorphic systems. Based on brain-like structures of 1D nanowire networks, neuromorphic synaptic devices can overcome the von Neumann bottleneck, achieving intelligent high-efficient sensing and computing function with high information processing rates and low power consumption. Here, high-temperature neuromorphic synaptic devices based on SiC@NiO core-shell nanowire networks optoelectronic memristors (NNOMs) are developed...

OBJECTIVE: To compare quantitative spectral parameters of visually-normal EEG between Mesial Temporal Lobe Epilepsy (MTLE) patients and healthy controls (HC). METHOD: We enrolled 26 MTLE patients and 26 HC. From each recording we calculated total power of all frequency bands and determined alpha-theta (ATR) and alpha-delta (ADR) power ratios in different brain regions. Group-wise differences between spectral parameters were investigated (p < 0.05). To test for associations between spectral-power and cognitive status, we evaluated correlations between neuropsychological tests and quantitative EEG (qEEG) metrics...

Previous research has shown that state anxiety facilitates stimulus-driven attentional capture and impairs goal-directed attentional control by increasing sensitivity to salient distractors or threat cues or narrowing spatial attention. However, recent findings in this area have been mixed, and less is known about how state-dependent anxiety may affect attentional performance. Here, we employed a novel dual-target search paradigm to investigate this relationship. This paradigm allowed us to investigate attentional control and how focus narrows under different anxiety states...

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods...

Memories of painful events constitute the basis for assessing patients' pain. This study explores the brain oscillatory activity during short-term memorization of a nociceptive stimulus. High-density EEG activity (128 electrodes) was recorded in 13 healthy subjects during a match-to-sample sensory discrimination task, whereby participants compared the intensity of a thumb-located electric shock (S2) with a prior stimulus to the same location (S1) delivered 8-10 s earlier. Stimuli were above or below the individual nociceptive threshold...

INTRODUCTION: Intelligent robots play a crucial role in enhancing efficiency, reducing costs, and improving safety in the logistics industry. However, traditional path planning methods often struggle to adapt to dynamic environments, leading to issues such as collisions and conflicts. This study aims to address the challenges of path planning and control for logistics robots in complex environments. METHODS: The proposed method integrates information from different perception modalities to achieve more accurate path planning and obstacle avoidance control, thereby enhancing the autonomy and reliability of logistics robots...

The unique optical and electrical properties of graphene-based heterojunctions make them significant for artificial synaptic devices, promoting the advancement of biomimetic vision systems. However, mass production and integration of device arrays are necessary for visual imaging, which is still challenging due to the difficulty in direct growth of wafer-scale graphene patterns. Here, a novel strategy is proposed using photosensitive polymer as a solid carbon source for in situ growth of patterned graphene on diverse substrates...

Expert fingerprint examiners demonstrate impressive feats of memory that may support their accuracy when making high-stakes identification decisions. Understanding the interplay between expertise and memory is therefore critical. Across two experiments, we tested fingerprint examiners and novices on their visual short-term memory for fingerprints. In Experiment 1, experts showed substantially higher memory performance compared to novices for fingerprints from their domain of expertise. In Experiment 2, we manipulated print distinctiveness and found that while both groups benefited from distinctive prints, experts still outperformed novices...

BACKGROUND AND OBJECTIVES: Research on driving ability in people with multiple sclerosis (MS) suggests that they might be at risk for unsafe driving due to MS-related motor, visual, and cognitive impairment. Our first aim was to investigate differences in driving ability and performance between people with MS (PwMS) and those without any neurologic or psychiatric disease ("controls"). Secondly, we determined disease-related factors influencing driving ability in PwMS. METHODS: We prospectively compared standardized performance in a driving simulator between 97 persons with early MS [mean (SD) = 6...

Measuring transient functional connectivity is an important challenge in Electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a novel methodology to overcome these problems called Filter Average Short-Term (FAST) functional connectivity. First, long-term, stable, functional connectivity is averaged across an entire study cohort for a given pair of Visual Short Term Memory (VSTM) tasks...

Projector video compensation aims to cancel the geometric and photometric distortions caused by non-ideal projection surfaces and environments when projecting videos. Most existing projector compensation methods start by projecting and capturing a set of sampling images, followed by an offline compensation model training step. Thus, abundant user effort is required before the users can watch the video. Moreover, the sampling images have little prior knowledge of the video content and may lead to suboptimal results...

Early detection of plant diseases is crucial for safeguarding crop yield, especially in regions vulnerable to food insecurity, such as Sub-Saharan Africa. One of the significant contributors to maize crop yield loss is the Northern Leaf Blight (NLB), which traditionally takes 14-21 days to visually manifest on maize. This study introduces a novel approach for detecting NLB as early as 4-5 days using Internet of Things (IoT) sensors, which can identify the disease before any visual symptoms appear. Utilizing Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) models, nonvisual measurements of Total Volatile Organic Compounds (VOCs) and ultrasound emissions from maize plants were captured and analyzed...

Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli...