An Eye Movement Classification Method based on Cascade Forest.

Eye tracking technology has become increasingly important in scientific research and practical applications. In the field of eye tracking research, analysis of eye movement data is crucial, particularly for classifying raw eye movement data into eye movement events. Current classification methods exhibit considerable variation in adaptability across different participants, and it is necessary to address the issues of class imbalance and data scarcity in eye movement classification.

Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept.

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users.

A simultaneous EEG-fNIRS dataset of the visual cognitive motivation study in healthy adults.

This article described a publicly available dataset of the visual cognitive motivation study in healthy adults. To gain an in-depth understanding and insights into motivation, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were measured simultaneously at shared locations while participants performed a visual cognitive motivation task. The participants' choices in the cognitive motivation task were recorded.

Cross Domain Correlation Maximization for Enhancing the Target Recognition of SSVEP-Based Brain-Computer Interfaces.

The target recognition performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces can be significantly improved with a training-based approach. However, the training procedure is time consuming and often causes fatigue. Consequently, the number of training data should be limited, which may reduce the classification performance.

Block Distributed Joint Temporal-Frequency-Phase Modulation for Steady-State Visual Evoked Potential Based Brain-Computer Interface With a Limited Number of Frequencies.

How to encode as many targets as possible with limited frequency resources is a grave problem that restricts the application of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In the current study, we propose a novel block-distributed joint temporal-frequency-phase modulation method for a virtual speller based on SSVEP-based BCI. A 48-target speller keyboard array is virtually divided into eight blocks and each block contains six targets.

The comparison of electroencephalography power and event related potential in success and failure during multitask game.

The first game-based treatment for children with attention-deficit hyperactivity disorder (ADHD) was approved by the United States Food and Drug Administration (FDA) in 2020. This game was developed for use at home as part of everyday training and can be used along with one's usual training plan. In this game, two tasks are performed in parallel: (1) a perceptual discrimination targeting task (response and not response and avoiding responding to sudden pop-up targets) and (2) a sensory-motor navigation task (players continuously adjust their location to interact with or avoid positional targets).

Spatio-Temporal Neural Dynamics of Observing Non-Tool Manipulable Objects and Interactions.

Previous studies have reported that a series of sensory-motor-related cortical areas are affected when a healthy human is presented with images of tools. This phenomenon has been explained as familiar tools launching a memory-retrieval process to provide a basis for using the tools. Consequently, we postulated that this theory may also be applicable if images of tools were replaced with images of daily objects if they are graspable (i.

Using Mean Pupil Diameter Change to Analyze Behavioral Performance in Multitasking Training Game.

To explore the actual behavioral performance of subjects in multitasking training games, we designed a VR game including a Target-tracking task (TTT) of continuously moving "Player" to track "Targets" and a randomly appearing Color-discrimination task (CDT) requiring discriminating whether "Player" and "Monster" have the same color, and recorded subjects' pupillary changes to reflect mental effort. By analyzing the mean pupil diameter change (MPDC) of different groups, we found that the high group presented pupil dilation during the post-event stage, reflecting that they engaged in psychological processing of CDT during the event, whereas the low group had no pupil dilation during part of the post-event stage, reflecting the possibility of ignoring the appearance of CDT, and such behaviors hardly raise good expectations for training effect. Our study suggests that MPDC mirrors not only the actual behavior of the different groups treating the multitasking paradigm, but also the influence of game design.

Effective Connectivity Analysis and Classification of Action Observation From Different Perspectives: An fMRI Study.

Objective: Analyzing the effective connectivity characteristics of brain networks in the process of action observation is helpful for understanding the neurodynamic mechanisms during action observation.

Method: In this study, functional magnetic resonance imaging (fMRI) images were obtained from 20 participants who performed hand-object interaction observation tasks from the first-person perspective (1PP) and third-person perspective (3PP). On the basis of a meta-analysis, 11 key brain regions were extracted as nodes to build an action observation network.

EEG-EMG Correlation Analysis with Linear and Nonlinear Coupling Methods Across Four Motor Tasks.

Correlation between brain and muscle signal is referred to as functional coupling. The amount of correlation between two signals greatly depends on the motor task performance. In this study, we designed the experimental paradigm with four types of motor tasks such as real hand grasping movement (RM), movement intention (Inten), motor imagery (MI) and only looking at virtual hand in three dimensional head mounted display (OL).

Electroencephalogram-Electromyogram Functional Coupling and Delay Time Change Based on Motor Task Performance.

Synchronous correlation brain and muscle oscillations during motor task execution is termed as functional coupling. Functional coupling between two signals appears with a delay time which can be used to infer the directionality of information flow. Functional coupling of brain and muscle depends on the type of muscle contraction and motor task performance.

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony.

The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar.

SSVEP-Based Brain-Computer Interface With a Limited Number of Frequencies Based on Dual-Frequency Biased Coding.

How to encode as many targets as possible with a limited-frequency resource is a difficult problem in the practical use of a steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) speller. To solve this problem, this study developed a novel method called dual-frequency biased coding (DFBC) to tag targets in a SSVEP-based 48-character virtual speller, in which each target is encoded with a permutation sequence consisting of two permuted flickering periods that flash at different frequencies. The proposed paradigm was validated by 11 participants in an offline experiment and 7 participants in an online experiment.

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis.

The present study aims to apply multiscale entropy (MSE) to analyse brain activity in terms of brain complexity levels and to use simultaneous electroencephalogram and functional near-infrared spectroscopy (EEG/fNIRS) recordings for brain functional analysis. A memory task was selected to demonstrate the potential of this multimodality approach since memory is a highly complex neurocognitive process, and the mechanisms governing selective retention of memories are not fully understood by other approaches. In this study, 15 healthy participants with normal colour vision participated in the visual memory task, which involved the making the executive decision of remembering or forgetting the visual stimuli based on his/her own will.

Auditory White Noise Affects Left/Right Visual Working Memory in an Opposite Pattern.

Adding auditory white noise (WN) to the environment has been considered to be a promising way to enhance the memory performance of children with attention deficit/hyperactivity disorder (ADHD) but disrupt that of non-ADHD children. To explore the exact mechanism behind WN benefits, we did a bilateral color-memory task with different WN conditions. A bilateral color-square array was displayed on one display.

Neural Activity and Decoding of Action Observation Using Combined EEG and fNIRS Measurement.

In a social world, observing the actions of others is fundamental to understanding what they are doing, as well as their intentions and feelings. Studies of the neural basis and decoding of action observation are important for understanding action-related processes and have implications for cognitive, social neuroscience, and human-machine interaction (HMI). In the current study, we first investigated temporal-spatial dynamics during action observation using a combined 64-channel electroencephalography (EEG) and 48-channel functional near-infrared spectroscopy (fNIRS) system.

Consistency and dynamical changes of directional information flow in different brain states: A comparison of working memory and resting-state using EEG.

Several recent studies have reported a frequency-dependent directional information flow loop in resting-state networks by phase transfer entropy, comprising an anterior-to-posterior information flow in the theta band and a posterior-to-anterior information flow in the alpha band. However, the functional roles of this information flow loop remain unclear. In the current study, we compared information flow patterns in four different brain states using electroencephalography: resting-state, fixation, working memory (WM) encoding and WM maintenance.

Investigating the Effect of Intrinsic Motivation on Alpha Desynchronization Using Sample Entropy.

The effect of motivation and attention could play an important role in providing personalized learning services and improving learners toward smart education. These effects on brain activity could be quantified by EEG and open the path to analyze the efficiency of services during the learning process. Many studies reported the appearance of EEG alpha desynchronization during the attention period, resulting in better cognitive performance.

Hybrid System for Engagement Recognition During Cognitive Tasks Using a CFS + KNN Algorithm.

Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed.

Relationship between rTMS effects and MEP features before rTMS.

Repetitive transcranial magnetic stimulation (rTMS) is a promising method for use in the clinical field, as it can induce modulation of cortical excitability. Generally, rTMS inhibits the motor cortex when delivered at less than 1 Hz. However, it has been indicated that a facilitative effect is induced by 1 Hz rTMS, depending on the stimulation parameters and the individual.

Measuring brain activation by using baseline-normalized event-related spectral perturbation in working memory task.

Understanding the cognitive function of human brain is an important step in providing scientific evidence which could help us improve the condition of memory disorders, slow down its progress or at least help the patients retain some important matters. In this study, we aimed to provide additional scientific evidence with more insight on how the brain functions at a good/bad cognitive state than the usual statistical analysis. We introduced the brain activation measurement using baseline-normalized ERSP to determine the activation of EEG data from stimuli.

EEG alpha power change during working memory encoding in adults with different memory performance levels.

Working memory (WM) capacity affects our daily life in many ways, and its decrease often associates with neural disorders (e.g. Alzheimer's disease).

A multichannel-near-infrared-spectroscopy-triggered robotic hand rehabilitation system for stroke patients.

There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation.

Evaluation of Cognitive Function When Hearing One's Own Name in Patients With Brain Injuries in Early Developmental Stages.

Purpose: The level of residual cognitive function in patients with early brain injury is a key factor limiting rehabilitation and the quality of life. Although understanding residual function is necessary for appropriate rehabilitation, the extent of its effects on cognitive improvement remains unknown. This study evaluated cognitive function in patients with severe motor and intellectual disabilities after early brain injuries due to cerebral hemorrhage or periventricular leukomalacia.