Basic and Clinical Neuroscience

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Showing 2 results for Resalat

Real Time Driver’s Drowsiness Detection by Processing the EEG Signals Stimulated with External Flickering Light

Amjad Hashemi, Valiallah Saba, Seyed Navid Resalat,
Volume 5, Issue 1 (Winter 2014 -- 2014)

Abstract

The objective of this study is development of driver’s sleepiness using Visually Evoked Potentials (VEP). VEP computed from EEG signals from the visual cortex. We use the Steady State VEPs (SSVEPs) that are one of the most important EEG signals used in human computer

interface systems. SSVEP is a response to visual stimuli presented. We present a classification method to discriminate between closed eyes and opened eyes. Fourier transforms and power spectrum density features extracted from signals and Multilayer perceptron and radial basis function neural networks used for classification. The experimental results show an accuracy of 97% for test data.

A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System

Seyed Navid Resalat, Valiallah Saba,
Volume 7, Issue 1 (Winter 2016 -- 2016)

Abstract

Introduction: Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications.
Methods: This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifier to select the best feature sets in the offline mode. The data set was recorded in 3-class tasks of the left hand, the right hand, and the foot motor imagery.
Results: The experimental results showed that Auto-Regressive (AR), Mean Absolute Value (MAV), and Band Power (BP) features have higher accuracy values,75% more than those for the other features.
Discussion: These features were selected for the designed real-time navigation. The corresponding results revealed the subject-specific nature of the MI-based BCI system however, the Power Spectral Density (PSD) based &alpha-BP feature had the highest averaged accuracy.

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