The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to si...

Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems. MI tasks performed by imagining doing a specific task and classifying through EEG signal processing. However, it is challenging to classify accurately. In this study, we propose LSTM-based classification framework enhance accuracy of four-class signals. To obtain time-varying data signals, sliding window tec...

The disease epilepsy is characterized by a sudden and recurrent malfunction of the brain that is termed seizer. The electroencephogram (EEG) has a lot of information about brain and also used in several automated epilepsy detection systems. In this study, the wavelet subband decomposition and Approximate Entropy (ApEn) is used for epilepsy detection from EEG signals. In first stage, EEG signals...

Brain Machine Interface (BMI) provides a digital link between the brain and a device such as a computer, robot or wheelchair. This paper presents a BMI design using Neuro-Fuzzy classifiers for controlling a wheelchair using EEG signals. EEG signals during motor imagery (MI) of left and right hand movements are recorded noninvasively at the sensorimotor cortex. Four mental task signals are analy...

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well nonmotor symptoms such anxiety depression. Biofeedback improves functions of patients regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or othe...

Despite considerable effort to remove it, the ballistocardiogram (BCG) remains a major artifact in electroencephalographic data (EEG) acquired inside magnetic resonance imaging (MRI) scanners, particularly in continuous (as opposed to event-related) recordings. In this study, we have developed a new Direct Recording Prior Encoding (DRPE) method to extract and separate the BCG and EEG components...

Objective: In this research, a new approach termed as “evolutionary-based brain map” is presented as a diagnostic tool to classify schizophrenic and control subjects by distinguishing their electroencephalogram (EEG) features.Methods: Particle swarm optimization (PSO) is employed to find discriminative frequency bands from different EEG channels. By deploying the energy of those selected fr...

In recent years, neural networks showed unprecedented growth that ultimately influenced dozens of different industries, including signal processing for the electroencephalography (EEG) process. Electroencephalography, although it appeared in first half 20th century, was not changed physical principles work to this day. But technology made significant progress area through use networks. many mod...

Prediction of memory performance (remembered or forgotten) has various potential applications not only for knowledge learning but also for disease diagnosis. Recently, subsequent memory effects (SMEs)-the statistical differences in electroencephalography (EEG) signals before or during learning between subsequently remembered and forgotten events-have been found. This finding indicates that EEG ...

This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The wavelet transform (HWT) method is designed by combination of discrete decomposition and packet transform. artifact suppression performed selection sub-bands obtained HWT. Fractional Gaussian noise (fGn) used as reference signal select containing artifacts. multi...