This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (...

In this paper, we describe three neural network (NN) based EEG-Speech (NES) models that map the unspoken EEG signals to the corresponding phonemes. Instead of using conventional feature extraction techniques, the proposed NES models rely on graphic learning to project both EEG and speech signals into deep representation feature spaces. This NN based linear projection helps to realize multimodal...

Classification of electroencephalograph (EEG) signals is the common denominator in EEG-based recognition systems that are relevant to many applications ranging from medical diagnosis to EEGcontrolled devices such as web browsers or typing tools for paralyzed patients. Here, we propose a new method for the classification of EEG signals. One of its core components projects EEG signals into a vect...

EEG apparatus are expensive and bulky. Their real-time performance is weak, and EEG signals are easy to be distorted. In this paper, a low-cost portable EEG signal acquisition system based on DSP is developed. By a noninvasive method with bipolar leads, weak EEG signals are induced to the pre-processing circuits, where they will undergo multi-level amplifying and filtering. Then, the analog sig...

Feature extraction and classification of EEG signals is core issues on EEG-based brain computer interface (BCI). Typically, such classification has been performed using signals from a set of selected EEG sensors. Because EEG sensor signals are mixtures of effective signals and noise, which has low signal-tonoise ratio, motor imagery EEG signals can be difficult to classification. In this paper,...

Feature extraction of EEG signals is core issues on EEG based brain mapping analysis. The classification of EEG signals has been performed using features extracted from EEG signals. Many features have proved to be unique enough to use in all brain related medical application. EEG signals can be classified using a set of features like Autoregression, Energy Spectrum Density, Energy Entropy, and ...

Electroencephalography (EEG), which contains cortical potentials during various mental processes, can be used to provide neural input signals to activate a brain machine interface (BMI). The effectiveness of such an EEG-based prosthetic system would rely on correct classification of executed motor signals from imagined motor movement signals; an executed motor signal should initiate movement in...

The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the com...

The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. T...