Correlaciones electrofisiológicas (EEG) de los efectos de recompensa en la percepción sensorial temprana en humanos

Existing research suggests that reward system and sensory perception networks function in concert and that activation in one may influence the other; however, the dynamics of these influences remains poorly understood. There is general agreement regarding the existence of an interaction between bottom-up and top-down signals in perception and attentional processing. While it’s not absolutely sure what stages of perception are influenced by cognition, it has been assumed that cognitive input influences later categorization stages of visual processing, and that earlier stages are solely involved in the pure bottom up extraction of basic features of sensory signals. Several recent experiments have challenged this idea by showing that top-down modulation by cognition may extend to early visual stages of perception. Recent Electrophysiological studies have begun to probe the neural correlates of the interaction between attention/reward, perception and cognitive control in humans. . We posit that selection of the value of our choices and actions from multiple alternatives may cause suppression of the sensory representations of unselected, low value stimuli while the selected, high value stimuli are enhanced. The present project has been proposed to study the dynamics of this selective process where effects of various reward categories on attention and early sensory perception are tracked using behavioral and electrophysiological (EEG) techniques. Methods from neuroscience, signal processing, psychophysics and EEG tools will be used in the project

Emergence of Confidence with Principles of Curiosity and Information Processing

Researchers in cognitive neuroscience, developmental cognitive neuroscience, educational psychology, educational technology, education theory, and other related fields collaborate in the emerging field of educational neuroscience, also known as neuroeducation. This interdisciplinary approach aims to explore the connections between psychological processes and education. By integrating basic discoveries in cognitive neuroscience with educational technology, researchers in neuroeducation strive to enhance curricula to foster curiosity and boost confidence levels in learners. The ultimate objective of neuroeducation is to generate both theoretical insights and practical applications that offer a fresh perspective on learning across various disciplines. The development of confidence is closely related to the principles of curiosity and information processing. When people are interested in a topic, they are more likely to seek out information and engage in learning experiences that can lead to deeper understanding. This information process allows people to analyze and understand the information they encounter, which in turn can increase their confidence in their knowledge and abilities. By embracing curiosity and honing their information-processing skills, people can develop a strong self-confidence that empowers them to take new challenges and pursue their goals with determination. This review study on curiosity have uncovered a fascinating insight into its mechanisms. Researchers found that curiosity aligns with a confidence function resembling an inverted U-shape, peaking when individuals have moderate confidence in their knowledge. Moreover, heightened curiosity drives individuals to actively seek out new information, showcasing the profound impact of curiosity on knowledge acquisition. This revelation holds immense promise for understanding human behavior and learning processes. This study focuses on boosting confidence in individuals through the application of curiosity and information knowledge processing techniques to elevate the standards of education and training across both traditional and modern methodologies. The integration of these principles is poised to revolutionize the learning experience, fostering a more dynamic and effective approach to knowledge acquisition and skill development. This innovative strategy holds immense potential to empower learners, educators, and trainers alike, paving the way for a more enriched and impactful educational landscape.

EEG BASED COGNITIVE WORKLOAD CLASSIFICATION DURING NASA MATB-II MULTITASKING

The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification

EEG INTERFACE MODULE FOR COGNITIVE ASSESSMENT THROUGH NEUROPHYSIOLOGIC TESTS

The cognitive signal processing is one of the important interdisciplinary field came from areas of life sciences, psychology, psychiatry, engi-neering, mathematics, physics, statistics and many other fields of research. Neurophysiologic tests are utilized to assess and treat brain injury, dementia, neurological conditions, and useful to investigate psychological and psychiatric disorders. This paper presents an ongoing research work on development of EEG interface device based on the principles of cognitive assessments and instrumentation. The method proposed engineering and science of cogni-tive signal processing in case of brain computer in-terface based neurophysiologic tests. The future scope of this study is to build a low cost EEG device for various clinical and pre-clinical applications with specific emphasis to measure the effect of cognitive action on human brain

Eeg based cognitive workload classification during nasa matb-ii multitasking

The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification

Eeg interface module for cognitive assessment through neurophysiologic tests

The cognitive signal processing is one of the important interdisciplinary field came from areas of life sciences, psychology, psychiatry, engi-neering, mathematics, physics, statistics and many other fields of research. Neurophysiologic tests are utilized to assess and treat brain injury, dementia, neurological conditions, and useful to investigate psychological and psychiatric disorders. This paper presents an ongoing research work on development of EEG interface device based on the principles of cognitive assessments and instrumentation. The method proposed engineering and science of cogni-tive signal processing in case of brain computer in-terface based neurophysiologic tests. The future scope of this study is to build a low cost EEG device for various clinical and pre-clinical applications with specific emphasis to measure the effect of cognitive action on human brain

Novel Notch Detection Algorithm for Detection of Dicrotic Notch in PPG Signals

Photoplethysmography (PPG) is a non-invasive optical technique that measures relative blood volume changes in the blood vessels and is widely used for research and physiological studies. Dicrotic notch represent the closure of the aortic semi-lunar valve and subsequent receding blood flow when ventricles relax. Their location is used to calculate systolic time intervals and monitor cardiac function. They play a significant role in early evaluation of various diseases such as sclerosis, occlusion, arterial spasm etc. This paper proposes a novel Notch Detection Algorithm (NDA) for detection of dicrotic notches from PPG signal that are measured by non-invasive photoplethysmography sensors. The proposed algorithm is implemented with the help of self developed Graphical User Interface (GUI) in MATLAB