Constructing non-invasive Brain-Computer Interfaces (BCI) that are practical for use in assistive technology has proven to be a challenging problem. We assert that classification algorithms that are capable of capturing sophisticated spatiotemporal patterns in Electroencephalography (EEG) signals are necessary in order for BCI to deliver fluid and reliable control. Since Echo State Networks (ES...

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were us...

Electroencephalogram (EEG), a record of electrical signal to represent the human brain activity, has great potential for the diagnosis to treatment of mental disorder and brain diseases such as epileptic seizure. Features extraction and classification of EEG signals is the crucial task to detect the stage of ictal (i.e., seizure period) and interictal (i.e., period between seizures) signals for...

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...

In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are thre...

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizur...

Objective. This paper presents the results obtained using a protocol based on special types of artificial neural networks (ANNs) assembled in a novel methodology able to compress the temporal sequence of electroencephalographic (EEG) data into spatial invariants for the automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects. With reference to the proce...

OBJECTIVE Studies have shown that low frequency components of brain recordings provide information on voluntary hand movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. APPROACH This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary h...

The aim of this paper is to develop the classification system using Artificial Neural Network for Electroencephalogram (EEG) signals. A good standard traditional method is to use Electroencephalogram for diagnosing patients brain functioning that corresponds to epilepsy and different brain disorders. This research focused on designing new classification techniques for single channel EEG recordi...

In this work, we analyse the Electroencephalogram (EEG) and tactile signals acquired during dynamic exploration of objects of seven different geometric shapes and observe that classification performance using features from both the domains together is better than using the either alone. Classification is done by Support Vector Machine and Naïve Bayesian (NB) classifiers using discrete wavelet t...