Source Localization and Spectrum Analyzing of EEG in Stuttering State upon Dysfluent Utterances.

The present study which addressed adults who stutter (AWS) attempted to investigate power spectral dynamics in the stuttering state by answering the questions using quantitative electroencephalography (qEEG). A 64-channel electroencephalography (EEG) setup was used for data acquisition at 20 AWS. Since the speech, especially stuttering, causes significant noise in the EEG, 2 conditions of speech preparation (SP) and imagined speech (IS) were considered.

How mental fatigue affects the neural sources of P300 component.

Existence of allocentric and egocentric systems for human navigation, mediating spatial, and response learning, respectively, has so far been discussed. It is controversial whether navigational strategies and their underlying learning systems and, accordingly, the activation of their associated brain areas are independent/parallel or whether they functionally/causally interact in a competitive or in a cooperative manner to solve navigational tasks. The insights provided by neural networks involved in reward-based navigation attributed to individual involvement or interactions of learning systems have been surveyed.

Separation of P300 event-related potential using time varying time-lag blind source separation algorithm.

Synchronous averaging over time locked single-trial of event-related potential (ERP) is known as the simplest scheme to extract P300 component. This method assumes the P300 features are invariant through the time while they are affected by factors like brain fatigue and habitation. In this study, a new scheme is proposed termed as time-varying time-lag blind source separation (TT-BSS) which is upon the second order statistics of signal to separate P300 waveform from the background electroencephalogram (EEG) while it captures the time variation of P300 component.

Usefulness of approximate entropy in the diagnosis of schizophrenia.

Objectives: Diagnosis of the psychiatric diseases is a bit challenging at the first interview due to this fact that qualitative criteria are not as accurate as quantitative ones. Here, the objective is to classify schizophrenic patients from the healthy subject using a quantitative index elicited from their electroencephalogram (EEG) signals.

Methods: Ten right handed male patients with schizophrenia who had just auditory hallucination and did not have any other psychotic features and ten age-matched right handed normal male control participants participated in this study.

Analysis of neural sources of p300 event-related potential in normal and schizophrenic participants.

The P300 event-related potential (ERP) is associated with attention and memory operations of the brain. P300 is changed in many cognitive disorders such as dementia, Alzheimer, schizophrenia, and major depression disorder. Therefore, investigation on basis of this component can help to improve our understanding of pathophysiology of such disorders and fundamentals of memory and attention mechanism.

Entropy and complexity measures for EEG signal classification of schizophrenic and control participants.

Objective: In this paper, electroencephalogram (EEG) signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of classifying the two groups.

Materials And Methods: For each case, 20 channels of EEG are recorded. Several features including Shannon entropy, spectral entropy, approximate entropy, Lempel-Ziv complexity and Higuchi fractal dimension are extracted from EEG signals.