background: this paper proposes a new emotional stress assessment system using multi-modal bio-signals. electroencephalogram (eeg) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. methods: we design an efficient acquisition protocol to acquire the eeg signals in five channels (fp1, fp2, t3, t4 and pz) and peripheral signals such as blood volu...

A Brain Computer Interface (BCI) is a new communication channel allows a person to control special computer applications like a computer cursor or robotic limb through the use of his/her thoughts. BCIs had become an active research area in the last decade. BCI research is based on recording and analyzing electroencephalographic (EEG) data and recognizing EEG patterns associated with various men...

An accurate measure of mental workload level has diverse neuroergonomic applications ranging from brain computer interfacing to improving the efficiency of human operators. In this study, we integrated electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), and physiological measures for the classification of three workload levels in an n-back working memory task. A significa...

Brain is one of the most complex organ of the humans, it controls the coordination of human muscles & nerves. EEG keeps its importance for identifying the physiological, and the psychological situations of the human and the functional activity of the brain. Being a non stationary signal, suitable analysis is essential for EEG to differentiate the normal EEG and epileptic seizures. Epilepsy is o...

A neural network based technique is presented, which is able to successfully extract polynomial classification rules from labeled electroencephalogram (EEG) signals. To represent the classification rules in an analytical form, we use the polynomial neural networks trained by a modified Group Method of Data Handling (GMDH). The classification rules were extracted from clinical EEG data that were...

MOTOR IMAGERY BASED EEG SIGNAL CLASSIFICATION USING SELF ORGANIZING MAPS *Muhammad Zeeshan Baig, Yasar Ayaz National University of Science and Technology Islamabad, Pakistan *Contact: [email protected] ABSTRACT: Classification of Motor Imagery (MI) tasks based EEG signals effectively is the main hurdle in order to develop online Brain Computer interface (BCI). In this research article, a re...

Disease identification is a major task in the field of biomedical. To perform it the analysis of EEG signal is to be performed. The proposed method presents for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD gives an effective time-frequency analysis of nonstationary signals. The intrinsic mode f...

The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The aim of this work is compare the automatic detection of EEG patterns using Discrete wavelet...

This study explores emotion recognition in videogames using electroencephalographic (EEG) data. Presently, emotion recognition using pattern recognition techniques has not yet been investigated in videogame play. This research is motivated by the possibility of retrieving insights into player experience from EEG signal during gameplay, which aims to contribute to Games User Research as an emerg...

The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classificatio...