Neural signature of attention impairment in allergic asthma: an ERP study.

We recruited 38 participants, equally distributed into healthy and asthma groups. Behavioral, neurophysiological, and lung function assessment tools were used in this study. Our behavioral data show that allergic asthma induces attention impairment.

Asthma induces psychiatric impairments in association with default mode and salience networks alteration: A resting-state EEG study.

Asthma is a chronic inflammatory disease associated with a high prevalence of psychiatric disorders. There are specific brain networks responsible for emotional processes, including two important networks associated with psychiatric problems: the default mode network (DMN), which is more active in the resting state, and the salience network (SN), which is structurally connected to DMN. Although previous studies suggested that neuro-phenotypes of asthma may be recognizable by the neural activity of brain circuits, an association between the brain's functional alterations and psychiatric impairments induced by asthma remains unknown.

Introducing a combined approach of empirical mode decomposition and PCA methods for maternal and fetal ECG signal processing.

Objectives: The purpose of this study was to evaluate the effectiveness of the non-linear adaptive combined approach on the analysis of maternal and fetal signals corrupted by noise.

Methods: Empirical mode decomposition (EMD) is a non-linear adaptive technic for data analysis and has been widely used in biomedical data. When we apply EMD on ECG signals, the number of modes that contain cardiac information may vary regarding the subject, type of the signal and recording conditions.

Assessment of human random number generation for biometric verification.

Random number generation is one of the human abilities. It is proven that the sequence of random numbers generated by people do not follow full randomness criteria. These numbers produced by brain activity seem to be completely nonstationary.

EEG-based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests.

Electro encephalography (EEG) is one of the most reliable sources to detect sleep onset while driving. In this study, we have tried to demonstrate that sleepiness and alertness signals are separable with an appropriate margin by extracting suitable features. So, first of all, we have recorded EEG signals from 10 volunteers.

Identification of voice disorders using long-time features and support vector machine with different feature reduction methods.

Identification of voice disorders has a fundamental role in our life nowadays. Therefore, many of these diseases must be diagnosed at early stages of occurrence before they lead to a critical condition. Acoustic analysis can be used to identify voice disorders as a complementary technique with other traditional invasive methods, such as laryngoscopy.