Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 5 de 5
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Sensors (Basel) ; 20(6)2020 Mar 19.
Artigo em Inglês | MEDLINE | ID: mdl-32204504

RESUMO

Inhibitory control is a cognitive process that inhibits a response. It is used in everyday activities, such as driving a motorcycle, driving a car and playing a game. The effect of this process can be compared to the red traffic light in the real world. In this study, we investigated brain connectivity under human inhibitory control using the phase lag index and inter-trial coherence (ITC). The human brain connectivity gives a more accurate representation of the functional neural network. Results of electroencephalography (EEG), the data sets were generated from twelve healthy subjects during left and right hand inhibitions using the auditory stop-signal task, showed that the inter-trial coherence in delta (1-4 Hz) and theta (4-7 Hz) band powers increased over the frontal and temporal lobe of the brain. These EEG delta and theta band activities neural markers have been related to human inhibition in the frontal lobe. In addition, inter-trial coherence in the delta-theta and alpha (8-12 Hz) band powers increased at the occipital lobe through visual stimulation. Moreover, the highest brain connectivity was observed under inhibitory control in the frontal lobe between F3-F4 channels compared to temporal and occipital lobes. The greater EEG coherence and phase lag index in the frontal lobe is associated with the human response inhibition. These findings revealed new insights to understand the neural network of brain connectivity and underlying mechanisms during human response inhibition.

2.
Sensors (Basel) ; 19(17)2019 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-31480570

RESUMO

Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.


Assuntos
Eletroencefalografia/métodos , Interfaces Cérebro-Computador , Potencial Evocado P300/fisiologia , Potenciais Evocados/fisiologia , Humanos
3.
Brain Sci ; 9(9)2019 Aug 27.
Artigo em Inglês | MEDLINE | ID: mdl-31461954

RESUMO

Auditory alarms are used to direct people's attention to critical events in complicated environments. The capacity for identifying the auditory alarms in order to take the right action in our daily life is critical. In this work, we investigate how auditory alarms affect the neural networks of human inhibition. We used a famous stop-signal or go/no-go task to measure the effect of visual stimuli and auditory alarms on the human brain. In this experiment, go-trials used visual stimulation, via a square or circle symbol, and stop trials used auditory stimulation, via an auditory alarm. Electroencephalography (EEG) signals from twelve subjects were acquired and analyzed using an advanced EEG dipole source localization method via independent component analysis (ICA) and EEG-coherence analysis. Behaviorally, the visual stimulus elicited a significantly higher accuracy rate (96.35%) than the auditory stimulus (57.07%) during inhibitory control. EEG theta and beta band power increases in the right middle frontal gyrus (rMFG) were associated with human inhibitory control. In addition, delta, theta, alpha, and beta band increases in the right cingulate gyrus (rCG) and delta band increases in both right superior temporal gyrus (rSTG) and left superior temporal gyrus (lSTG) were associated with the network changes induced by auditory alarms. We further observed that theta-alpha and beta bands between lSTG-rMFG and lSTG-rSTG pathways had higher connectivity magnitudes in the brain network when performing the visual tasks changed to receiving the auditory alarms. These findings could be useful for further understanding the human brain in realistic environments.

4.
Front Hum Neurosci ; 10: 185, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27199708

RESUMO

The stop-signal paradigm has been widely adopted as a way to parametrically quantify the response inhibition process. To evaluate inhibitory function in realistic environmental settings, the current study compared stop-signal responses in two different scenarios: one uses simple visual symbols as go and stop signals, and the other translates the typical design into a battlefield scenario (BFS) where a sniper-scope view was the background, a terrorist image was the go signal, a hostage image was the stop signal, and the task instructions were to shoot at terrorists only when hostages were not present but to refrain from shooting if hostages appeared. The BFS created a threatening environment and allowed the evaluation of how participants' inhibitory control manifest in this realistic stop-signal task. In order to investigate the participants' brain activities with both high spatial and temporal resolution, simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings were acquired. The results demonstrated that both scenarios induced increased activity in the right inferior frontal gyrus (rIFG) and presupplementary motor area (preSMA), which have been linked to response inhibition. Notably, in right temporoparietal junction (rTPJ) we found both higher blood-oxygen-level dependent (BOLD) activation and synchronization of theta-alpha activities (4-12 Hz) in the BFS than in the traditional scenario after the stop signal. The higher activation of rTPJ in the BFS may be related to morality judgments or attentional reorienting. These results provided new insights into the complex brain networks involved in inhibitory control within naturalistic environments.

5.
Conf Proc IEEE Eng Med Biol Soc ; 2016: 5857-5860, 2016 08.
Artigo em Inglês | MEDLINE | ID: mdl-28269586

RESUMO

In this paper, a method is proposed to predict the resting-state outcomes of participants based on their electroencephalogram (EEG) signals recorded before the successful /unsuccessful response inhibition. The motivation of this study is to enhance the shooter performance for shooting the target, when their EEG patterns show that they are ready. This method can be used in brain-computer interface (BCI) system. In this study, multi-channel EEG from twenty participants are collected by the electrodes placed at different scalp locations in resting-state time. The EEG trials are used to predict two possible outcomes: successful or unsuccessful stop. Four classifiers (QDC, KNNC, PARZENDC, LDC) are used in this study to evaluation the accuracy of our system. Based on the collected time-domain EEG signals, the phase locking value (PLV) from 5-pair electrodes are calculated and then used as the feature input for the classifiers. Our experimental results show that the proposed method prediction accuracy (leave-one-out) was obtained 95% by QDC classifier.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Adulto , Análise Discriminante , Eletrodos , Voluntários Saudáveis , Humanos , Masculino , Estimulação Luminosa , Tempo de Reação , Adulto Jovem
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA