Multiscale sample entropy (MSE) of human electroencephalogram (EEG) data from patients under different pathological conditions of Alzheimer’s disease (AD) was evaluated to measure the complexity of the signal. Quantifying the complexity level with respect to various temporal scales, MSE analysis provides a dynamical description of AD development. When compared to EEG data from normal subjects, ...

Sleep stages are mainly classified via electroencephalogram (EEG) which involves some prominent characters not only in amplitude but also in frequency. What is more, researchers are using computer assisted analysis to acquire the panoramic view of long duration sleep EEG. However, unlike the empirical judgment, it is fairly difficult to decide the specific values of sleep rules for computer bas...

Electroencephalogram (EEG) provides a non-invasive way to analyze brain activity. Blinking and movement of the eyes causes a strong electrical activity that can contaminate EEG recordings, particularly around the forehead but also as far as in occipital areas. Removal of such ocular artifacts is a considerable signal processing problem, since those artifacts overlap in frequency domain with EEG...

Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded s...

The objective of present work was to assess differences in spectrum, coherence, and phase synchrony of topical electroencephalogram (EEG) between alcohol-dependent individuals and healthy participants. Surface currents were mitigated by a common average spatial filter. Parametric spectral and coherence estimates obtained for consecutive 0.5s-long EEG fragments were generally lower for alcoholic...

Emotion recognition is one of the most important research directions in field brain–computer interface (BCI). However, to conduct electroencephalogram (EEG)-based emotion recognition, there exist difficulties regarding EEG signal processing; moreover, performance classification models this regard restricted. To counter these issues, 2022 World Robot Contest successfully held an affective BCI co...

Electroencephalogram (EEG) recordings are often contaminated with ocular and muscle artifacts. In this paper, the canonical correlation analysis (CCA) is used as blind source separation (BSS) technique (BSS-CCA) to decompose the artifact contaminated EEG into component signals. We combine the BSSCCA technique with wavelet filtering approach for minimizing both ocular and muscle artifacts simult...

Electroencephalogram (EEG) data depict various emotional states and reflect brain activity. There has been increasing interest in EEG emotion recognition brain–computer interface systems (BCIs). In the World Robot Contest (WRC), BCI Controlled successfully staged an technology competition. Three types of emotions (happy, sad, neutral) are modeled using signals. this study, 5 methods employed by...

A wireless recording system was developed to study the electroencephalogram (EEG) in unrestrained, male Landrace piglets. Under general anesthesia, ball-tipped silver/silver chloride electrodes for EEG recording were implanted onto the dura matter of the parietal and frontal cortex of the piglets. A pair of miniature preamplifiers and transmitters was then mounted on the surface of the skull. T...

This work presents a recognition system for epileptiform abnormalities based on electroencephalogram (EEG) analysis. The proposed system combines a Support Vector Machine classifier automatically trained by an implementation of machine learning approach known as Bag of Words.