Quantitative EEG Features Selection in the Classification of Attention and Response Control in the Children and Adolescents with Attention Deficit Hyperactivity Disorder

Bashiri, Azadeh; Shahmoradi, Leila; Beigy, Hamid; Savareh, Behrouz Alizadeh; Nosratabadi, Mehdi; Kalhori, Sharareh R. Niakan; Ghazisaeedi, Marjan

doi:10.4155/fsoa-2017-0138

Cited by 14 publications

(5 citation statements)

References 53 publications

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“…This phase aims to reduce interference of individual differences in EEG data. 15,16 Thirty seconds of rest keeps all participants in a more stable state, making it easier to adapt to simulated scenarios and more reliable the collected data. 17 Next, the VR device will be debugged, and the device image and sound will be adjusted according to the different personal characteristics of each subject.…”

Section: Methodsmentioning

confidence: 99%

A comprehensive experimental framework based on analysis of the pilot’s EEG and NASA-TLX questionnaire in a VR environment

Zhang

et al. 2023

Proc Inst Mech Eng H

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The relationship framework between electroencephalogram (EEG) signals and subjective perception during pilots’ missions was established to improve pilots’ training efficiency and flight safety. Primarily, this study constructs a real flight scene through virtual reality (VR) and then obtains EEG data in simulated scenes. Researchers use VR technology to build a mission simulation room and then acquire EEG data from participants wearing EEG acquisition devices in the simulated room. The experimental process is divided into flight simulation and a questionnaire survey. Based on the participants’ EEG analysis, the researchers verified the changes of β rhythm under a high-difficulty operation mission. In addition, this study infers the mechanism of affecting pilots’ mental workload under high-difficulty operation by analyzing the correlation between subjective questionnaire results and β rhythms. The results showed that in the context of pilots performing flight missions in the aircraft space environment, the pilots’ mental load had the most excellent rhythmic relationship with the regions representing β rhythm. Therefore, a comprehensive experimental framework constructed in this study based on virtual simulation space to analyze the relationship between EEG and NASA-TLX subjective questionnaire provides a set of more accurate reference information for the design of a pilot training system based on pilot training efficiency and flight safety.

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Section: Methodsmentioning

confidence: 99%

A comprehensive experimental framework based on analysis of the pilot’s EEG and NASA-TLX questionnaire in a VR environment

Zhang

et al. 2023

Proc Inst Mech Eng H

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“…Recently, IVA-CPT as a computerized test has been provided on the basis of DSM-IV criteria for assessing two universal scales (Scale Response Control and Scale Attention Quotient) [ 4 , 14 ]. According to IVA-CPT, each type of attention originates from different parameters, which are mentioned as follows: Focused Attention: assessed primarily by the vigilance and prudence scales Sustained Attention: IVA + Plus scales related to this element are stamina, consistency, and focus Selective Attention: prudence, vigilance, and comprehension scales are reflective of this element.…”

Section: Methodsmentioning

confidence: 99%

Section: Iva-cptmentioning

confidence: 99%

A Novel Method Based on ERP and Brain Graph for the Simultaneous Assessment of Various Types of Attention

Jami

Khalilzadeh

Ghoshuni

et al. 2022

Computational Intelligence and Neuroscience

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Assessment of attention is of great importance as one of human cognitive abilities. Although neuropsychological tests have been developed and used to evaluate the ability to pay attention, their validity and reliability have been reduced due to some limitations such as the presence of intervention factors, including human factors, limited range of languages, and cultural influences. Therefore, direct outputs of the brain system, represented by event-related potentials (ERPs), and the analysis of its function in cognitive activities have become very important as a complementary tool to assess various types of attention. This research tries to assess 4 types of attention including sustained, alternative, selective, and divided, using an integrated visual-auditory test and brain signals simultaneously. Thus, the electroencephalogram (EEG) data were recorded using 19 channels, and the integrated visual and auditory (IVA-AE) test was simultaneously performed on twenty-eight healthy volunteers including 22 male and 6 female subjects with the average age of 27 ± 5.3 years. Then ERPs related to auditory and visual stimuli with synchronous averaging technique were extracted. A topographic brain mapping (topo-map) was plotted for each frame of stimulation. Next, an optical flow method was implemented on different topo-maps to obtain motion vectors from one map to another. After obtaining the overall brain graph of an individual, some features were extracted and used as measures of local and global connectivity. The extracted features were consequently evaluated along with the parameters of the IVA test by support vector machine regression (SVM-R). The volume of attention was then quantified by combining the IVA parameters. Ultimately, estimation accuracy of each type of attention including focused attention (86.1%), sustained attention (83.4%), selective attention (80.9%), and divided attention (79.9%) was obtained. According to the present study, there is a significant relationship between response control and attention indicators of the IVA test as well as ERP brain signals.

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“…Tanto el electroencefalograma clínico (EEG) (10) como el cuantitativo (QEEG) (11) realizados en estado de reposo con ojos cerrados (12), en reposo con ojos abiertos (13), durante la ejecución de una tarea de atención (14) o durante la ejecución de la prueba TOVA (15) han aportado información controvertida de la actividad eléctrica cerebral y de la sintomatología cognitiva y comportamental de los niños con TDAH. La evidencia del QEEG y las fallas en la ejecución de pruebas que miden atención, han permitido apoyar la idea de que los niños con TDAH tienen una forma de maduración diferente al niño sano (16), la cual se ha manifestado en la disminución de la Potencia Absoluta del ritmo Beta (11,17,18).…”

Section: Introductionunclassified

“…En un estudio, los autores (34) encontraron que la actividad Beta varía en las diferentes áreas corticales, siendo la derivación frontal izquierda (F7) el mejor sitio para confirmar el diagnóstico de TDAH, con una sensibilidad de 86% y especificidad de 57.% Sin embargo, también se ha informado concurrente a inatención, el incremento PA de Beta total (35); así como el incremento de PA Beta en todas las áreas corticales, menos en el área occipital (36), o bien disminución en la región posterior e incremento en la región Frontal (35). Con el uso de la prueba estadística, Redes Neuronales Perceptrón Multicapas (RNPM), se encontró que el valor de la PA de Beta alta, Beta 1 y Beta 2, está dentro de las sesenta mejores medidas para vincular la disfunción en el QEEG con la existencia de defectos en atención y el control de la respuesta en niños con TDAH (18,37). Usando el mismo modelo estadístico, encontraron que la PA Beta del registro del QEEG durante la ejecución de una tarea de atención, predice en más del 93% una ejecución deficiente.…”

Section: Introductionunclassified

Distribución cortical de la potencia absoluta de la actividad Beta 12Hz-25 Hz en niños varones con trastorno por déficit de atención e hiperactividad combinado

Téllez-Villagra¹,

Avilés²

2021

Rev Psiquiatr Infanto-Juv

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Introducción: El ritmo Beta del electroencefalograma cuantitativo (QEEG) está vinculado con inatención y alteraciones del movimiento. En niños con trastorno por déficit de atención e hiperactividad (TDAH) se han reportado potencia absoluta (PA) con incremento en frecuencias lentas y disminución en rápidas especialmente Beta-total. Objetivo: Identificar la distribución cortical de PA disminuida o incrementada en el QEEG en reposo-ojos-cerrados de cada frecuencia Beta (12Hz-25Hz) como predictora de inatención visual o auditiva y de la iniciación e inhibición del movimiento en niños varones con TDAH de presentación combinada. Material y Métodos: Estudio retrospectivo (2008-2019) en 131 niños varones (6-14 años), diagnosticados de TDAH de presentación combinada. De cada niño, se obtuvieron 532 datos: PA + 2 de la norma (base Neuroguide), Beta (12-25Hz) en 19 derivaciones del QEEG se asociaron a inatención visual, auditiva y al movimiento (puntuación < 80 TOVA-Visual y Auditiva). Resultados: Se obtuvo una PA disminuida en 1738 derivaciones (81,5%); PA incrementada en 394 (18,48%). Beta 20-25Hz PA disminuida predominó en Frontal y Centro-témporo-occipital; 12-13Hz PA-incrementada en Parietal. Inatención visual más baja que auditiva. Variabilidad y Tiempo de Respuesta visual caracterizaron la mala ejecución. PA-disminuida Beta 25Hz en Frontal caracterizó 30 (43%) niños con inatención visual y auditiva; Beta 23-25Hz en Centro-témporo-occipital a 33 (75%) con inatención visual; PA-incrementada 21Hz en Frontal y 25Hz en Parietal a 2 (29%) con inatención-auditiva. Beta 13-25Hz PA-disminuida en Frontal y Centro-témporo-occipital y 20-25Hz en Parietal influyeron en inatención visual en todas sus variables; mientras que inatención-auditiva en todas sus variables fue influenciada por Beta 16-25Hz en Centro-témporo-occipital. Beta 16-25Hz PA-disminuida en Frontal y Centro-témporo-occipital influyeron en hiperactividad visual y auditiva; Beta 22-25Hz en Centro-témporo-occipital en impulsividad visual y auditiva. Conclusión: Beta 20-25Hz con PA disminuida en Centro-témporo-occipital y 12-13Hz con PA-incrementada en Parietal junto con Variabilidad y Tiempo de Respuesta visual, pudieran ser biomarcadores del TDAH combinado. Los biomarcadores podrán apoyar el diagnóstico preciso y el uso de terapia no farmacológica con tecnología de punta que regule la actividad eléctrica.

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Quantitative EEG Features Selection in the Classification of Attention and Response Control in the Children and Adolescents with Attention Deficit Hyperactivity Disorder

Cited by 14 publications

References 53 publications

A comprehensive experimental framework based on analysis of the pilot’s EEG and NASA-TLX questionnaire in a VR environment

A comprehensive experimental framework based on analysis of the pilot’s EEG and NASA-TLX questionnaire in a VR environment

A Novel Method Based on ERP and Brain Graph for the Simultaneous Assessment of Various Types of Attention

Distribución cortical de la potencia absoluta de la actividad Beta 12Hz-25 Hz en niños varones con trastorno por déficit de atención e hiperactividad combinado

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