In normal elderly subjects, the best electroencephalogram (EEG)-based predictor of cognitive impairment is theta EEG activity abnormally high for their age. The goal of this work was to explore the effectiveness of a neurofeedback (NFB) protocol in reducing theta EEG activity in normal elderly subjects who present abnormally high theta absolute power (AP). Fourteen subjects were randomly assigned to either the experimental group or the control group; the experimental group received a reward (tone of 1000 Hz) when the theta AP was reduced, and the control group received a placebo treatment, a random administration of the same tone. The results show that the experimental group exhibits greater improvement in EEG and behavioral measures. However, subjects of the control group also show improved EEG values and in memory, which may be attributed to a placebo effect. However, the effect of the NFB treatment was clear in the EG, although a placebo effect may also have been present.

In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

Electroencephalographic alterations have been reported in subjects with learning disorders, but there is no consensus on what characterizes their electroencephalogram findings. Our objective was to determine if there were subgroups within a group of scholars with not otherwise specified learning disorders and if they had specific electroencephalographic patterns. Eighty-five subjects (31 female, 8–11 years) who scored low in at least two subscales -reading, writing and arithmetic- of the Infant Neuropsychological Evaluation were included. Electroencephalograms were recorded in 19 leads during rest with eyes closed; absolute power was obtained every 0.39 Hz. Three subgroups were formed according to children’s performance: Group 1 (G1, higher scores than Group 2 in reading speed and reading and writing accuracy), Group 2 (G2, better performance than G1 in composition) and Group 3 (G3, lower scores than Groups 1 and 2 in the three subscales). G3 had higher absolute power in frequencies in the delta and theta range at left frontotemporal sites than G1 and G2. G2 had higher absolute power within alpha frequencies than G3 and G1 at the left occipital site. G3 had higher absolute power in frequencies in the beta range than G1 in parietotemporal areas and than G2 in left frontopolar and temporal sites. G1 had higher absolute power within beta frequencies than G2 in the left frontopolar site. G3 had lower gamma absolute power values than the other groups in the left hemisphere, and gamma activity was higher in G1 than in G2 in frontopolar and temporal areas. This group of children with learning disorders is very heterogeneous. Three subgroups were found with different cognitive profiles, as well as a different electroencephalographic pattern. It is important to consider these differences when planning interventions for children with learning disorders.

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The sensorimotor rhythm (SMR) is an electroencephalographic rhythm associated with motor and cognitive development observed in the central brain regions during wakefulness in the absence of movement, and it reacts contralaterally to generalized and hemibody movements. The purpose of this work was to characterize the SMR of 4-month-old infants, born either healthy at term or prematurely with periventricular leukomalacia (PVL). Two groups of infants were formed: healthy and premature with PVL. Their electroencephalograms (EEGs) were recorded in four conditions: rest, free movement, right-hand grasping and left-hand grasping, in order to explore general reactivity to free movement and contralateral reactivity in hand-grasping conditions. Associations between SMR, and cognitive and motor performance were analyzed. The healthy infants showed a SMR between 5.47 and 7.03 Hz, with clear contralateral reactivity to free movement and right-hand grasping. However, the premature infants with PVL did not show enough electroencephalographic characteristics to evidence the presence of SMR. Poor performance, characteristic of children with PVL, was related to low-frequency SMR, while good performance was associated with a higher frequency rhythm in the left hemisphere. The presence of SMR in the group of healthy infants could be considered a sign of health at this age. Thus, poor SMR evidence in the EEG of infants with PVL is probably a sign of brain immaturity or brain dysfunction. Our results provide data on infant SMR development that is needed to design neurofeedback protocols for infants with PVL.