This paper investigates mental workload assessment using statistical features derived from electroencephalography (EEG) signals. Mean, root mean squared, and correlation-based features are extracted from data including EEG signal recordings of five participants performing a reading task with three difficulty levels of low, medium, and high and a baseline condition. Results reveal that for the g...

Classification of electroencephalograph (EEG) data is the common denominator in various recognition tasks related to EEG signals. Automated recognition systems are especially useful in cases when continuous, long-term EEG is recorded and the resulting data, due to its huge amount, cannot be analyzed by human experts in depth. EEG-related recognition tasks may support medical diagnosis and they ...

EEG signal analysis is applied in various fields such as medicine, communication and control. To control based on EEG signals achieved good result, the system must identify effectively EEG signals. In this paper, a novel approach proposes the EEG signal identification based on image with the EEG signal processing via Wavelet transform and the identification via single-layer neural network. The ...

Electroencephalogram (EEG) signals extracted during imagined activities have been studied for use in Brain Computer Interface (BCI) applications. The major hurdle in the EEG based BCI is that the EEG signals are unique to each individual. This complicates a universal BCI design. On the contrary, this disadvantage is the advantage when it comes to using EEG signals from imagined activities for b...

EEG contains a wealth of physiological, psychological and pathological information, EEG signal analysis and processing are either in clinical of some brain disease diagnosis and treatment, or in the cognitive science research fields are very important. According to the basic characteristics of EEG signal, discusses the EEG signal acquisition principle, method and the design train of thought. Pr...

The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...

Brain-computer interface (BCI) systems provide a new communication and control channel between people and external devices [1]. In these systems, users can control an external device using their intention or imagination without making any real muscle movement. Therefore, these systems are very helpful for people who are suffering from severe motor diseases. The electroencephalogram (EEG) is wid...

Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted corrupt information contained in signals degrade performance of qualitative analysis clinical applications well EEG-based brain–computer interfaces (BCIs). The wavelet transform denoising are increasing day by due to its capability handling non-stationary ...

Electroencephalogram (EEG) recording systems have been frequently used as the sources of information in diagnosis of epilepsy by several researchers. In this study, rearranged EEG signals were classified by Multilayer Perceptron -eural -etwork (MLP--) model. Used data consists of five groups (A, B, C, D, and E) each containing 100 EEG segments. In this study, center points with equal interval w...

The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, c...