Nonlinear dynamics measures applied to EEG recordings of patients with Attention Deficit/Hyperactivity Disorder: Quantifying the effects of a neurofeedback treatment

Abstract

This work presents the application of nonlinear dynamics measures to electroencephalograms (EEG) acquired from patients with Attention Deficit/Hyperactivity Disorder (ADHD) before and after a neurofeedback therapy, with the aim to assess the effects of the neurofeedback in a quantitative way. The database contains EEG registers of seven patients acquired in eyes-closed and eyes-opened conditions, in pre-and post-treatment phases. Five measures were applied: largest Lyapunov exponent, Lempel-Ziv complexity, Hurst exponent, and multiscale entropy on two different scales. The purpose is to test whether these measures are apt to detect and quantify differences from EEG registers between pre- and post-treatment. The results indicate that these measures could have a potential utility for detection of quantitative changes in specific EEG channels. In addition, the performance of some of these measures improved when the bandwidth was reduced to 3-30 Hz.

Complexity Analysis of Resting-State fMRI in Adult Patients with Attention Deficit Hyperactivity Disorder: Brain Entropy

“…The degree of complexity is associated with the number of brain connections; decreased connectivity indicates lower complexity, and increased connectivity reflects greater complexity. Numerous studies on brain activity in ADHD have been performed using functional neuroimaging techniques other than fMRI, such as electroencephalography and magnetoencephalography [ 32 , 33 ]. Gómez et al [ 32 ] demonstrated that MEG recordings of ADHD patients were more-regular compared to recordings obtained in a control group; furthermore, there were significant differences among these groups in five brain regions, that is, anterior, central, posterior, left lateral, and right lateral areas.…”

Neurofeedback, aplicaciones y eficacia

“…En otro estudio se encontró la aplicación no lineal de medidas dinámicas del EEG realizada en sesiones pre y post a pacientes con Trastorno por Déficit de Atención con Hiperac tividad (TDAH) (Cerquera, Arns, Buitrago, Gutiérrez & Freund, 2012). Uno de los objetivos de este estudio colombiano es revisar la efectividad de cinco medidas basadas en técnicas de dinámica no lineal, para detectar diferencias en los registros de EEG de pacientes con TDAH antes y después de la terapia de neurofeedback.…”

“…En sus conclusiones los autores plantean que entre los 26 canales de respuesta que generalmente posee un registro EEG, el tres es el más representativo, teniendo en cuenta que esta interpretación cambiará en la medida en que se establezca un objetivo de evaluación particular (Cerquera et al, 2012).…”

Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses

“…Both SI and LZC have been employed in several studies aiming to characterize neurological states using EEG signals. 8,[14][15][16][17][18][19][20][21][22][23][24][25][26] Nevertheless, a key issue in the application of these and other nonlinear measures is their sensitivity to nonstationarities in the time series, which is characterized by variations over time of their statistical properties. 27 In case of EEG signals, nonstationarity episodes could be caused mainly by the sensitivity of dynamical parameters to the different time scales in brain activity, 28,29 most likely reflecting dynamical nonstationarity.…”

“…Several studies employing SI and LZC are available in the current literature, where authors have assumed durations of epochs to fulfil quasistationarity requirements covering values between 1 and 300 seconds. 13,[15][16][17][18][19][20][21][22][23][24]26,[39][40][41][42][43][44][45][46][47] In addition to the search of quasi-stationarity conditions, the number of data points also affects the nonlinear deterministic dynamics of these epochs; this is proportional to the sampling rate utilized in the acquisition of the signal, and too short data sets imply errors in the calculation of nonlinear metrics. Although under certain conditions this length should be larger than 100 000 data points according to criterion proposed by Eckmann and Ruelle 48 for generic time series, Gallez and Babloyantz 49 established that data sets larger than 20 000 data points can be suitable for evaluation of Lyapunov exponents in alpha activity, which is a nonlinear measure that quantifies the chaoticity in a time series.…”