Dataset Information

Toward understanding the neurophysiological basis of peripersonal space: An EEG study on healthy individuals.

ABSTRACT: The subcortical mechanisms subtending the sensorimotor processes related to the peripersonal space (PPS) have been well characterized, whereas less evidence is available concerning the cortical mechanisms. We investigated the theta, alpha and beta event-related spectral perturbations (ERSP) while holding the forearm in different positions into the PPS of the face. Fifty healthy individuals were subjected to EEG recording while being provided with median nerve electric stimulation at the wrist of the right hand held at different hand-to-face distances. Theta and beta rhythms were significantly perturbed depending on the hand-to-face distance, whereas alpha oscillations reflected a more general, non-specific oscillatory response to the motor task. The perturbation of theta and beta frequency bands may reflect the processes of top-down modulation overseeing the conscious spatiotemporal encoding of sensory-motor information within the PPS. In other words, such perturbation reflects the continuous update of the conscious internal representations of the PPS to build up a purposeful and reflexive motor response.

SUBMITTER: Naro A

PROVIDER: S-EPMC6590804 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Toward understanding the neurophysiological basis of peripersonal space: An EEG study on healthy individuals.

Naro Antonino A Calabrò Rocco Salvatore RS La Rosa Gianluca G Andronaco Veronica Agata VA Billeri Luana L Lauria Paola P Bramanti Alessia A Bramanti Placido P

PloS one 20190624 6

The subcortical mechanisms subtending the sensorimotor processes related to the peripersonal space (PPS) have been well characterized, whereas less evidence is available concerning the cortical mechanisms. We investigated the theta, alpha and beta event-related spectral perturbations (ERSP) while holding the forearm in different positions into the PPS of the face. Fifty healthy individuals were subjected to EEG recording while being provided with median nerve electric stimulation at the wrist of ...[more]

PMID: 31233542

Similar Datasets

Project description:Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis remains essentially clinical, based on history and exam, with no available biomarkers. In this paper, we describe a convolutional neural network (CNN) with a four-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG time-frequency decompositions (spectrograms) of electroencephalography data (EEG), particularly of event-related potentials (ERP) from ADHD patients (n = 20) and healthy controls (n = 20) collected during the Flanker Task, with 2800 samples for each group. We treat the data as in audio or image classification approaches, where deep networks have proven successful by exploiting invariances and compositional features in the data. The model reaches a classification accuracy of 88% ± 1.12%, outperforming the Recurrent Neural Network and the Shallow Neural Network used for comparison, and with the key advantage, compared with other machine learning approaches, of avoiding the need for manual selection of EEG spectral or channel features. The event-related spectrograms also provide greater accuracy compared to resting state EEG spectrograms. Finally, through the use of feature visualization techniques such as DeepDream, we show that the main features exciting the CNN nodes are a decreased power in the alpha band and an increased power in the delta-theta band around 100 ms for ADHD patients compared to healthy controls, suggestive of attentional and inhibition deficits, which have been previously suggested as pathophyisiological signatures of ADHD. While confirmation with larger clinical samples is necessary, these results suggest that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, highlighting the potential of this methodology to develop biomarkers of practical clinical utility.

Dataset Information

Toward understanding the neurophysiological basis of peripersonal space: An EEG study on healthy individuals.

Publications

Toward understanding the neurophysiological basis of peripersonal space: An EEG study on healthy individuals.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets