GT5 Biomedical Signal

Behnaz Ghoraani's picture
Behnaz Ghoraani
Florida Atlantic University (USA)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 14:00 to 15:30
Towards multi-parametric hub scoring of functional cortical brain networks: An electroencephalographic (EEG) study across lifespan
The attractiveness and robustness of graph theory has stimulated an unprecedented increase in studies aiming to understand the functional organization and dynamics of the brain. The investigation of brain connectomics produces a tremendous amount of data which may be examined at both a macroscopic and microscopic level. However, the interpretation of findings is still challenging. Novel methodological approaches should enhance the arsenal of the tools employed towards the understanding of the interaction of the distinct brain components.
Vasiliki Zilidou's picture
Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Ana Vivas's picture
Ana Vivas
Maria Karagianni's picture
Maria Karagianni
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data
Discriminating the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated for their ability to discriminate preictal from interictal EEG segments. Using public EEG data, a wide range of features is extracted from each segment and then applied to the classifiers.
Kostas M. Tsiouris's picture
Kostas M. Tsiouris
National Technical University of Athens (GR)
Vasileios C. Pezoulas's picture
Vasileios C. Pezoulas
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Michalis Zervakis's picture
Michalis Zervakis
Detection of Alcoholism based on EEG Signals and Functional Brain Network Features Extraction
Alcohol abuse disorder or alcoholism is a common disorder that leads to brain defects and associated cognitive, emotional and behavioural impairments. Finding and extracting discriminative biological markers, which are correlated to healthy brain pattern and alcoholic brain pattern, helps us to utilize automatic methods for detecting and classifying alcoholism. Many brain disorders could be detected by analyzing the Electroencephalography (EEG) signals. In this paper, for extracting the required markers we analyze the EEG signals for two groups of alcoholic and control subjects.
Negar Ahmadi's picture
Negar Ahmadi
Yulong Pei's picture
Yulong Pei
Technische Universiteit Eindhoven (NL)
Mykola Pechenizkiy's picture
Mykola Pechenizkiy
EEG Classification and Short-Term Epilepsy Prognosis using Brain Computer Interface Software
The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted and a classification algorithm is trained. The training procedure extracts knowledge in terms of a classification model, which is employed in a real-time testing.
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Konstantinos Zoulis's picture
Konstantinos Zoulis
Euripidis Glavas's picture
Euripidis Glavas
Technological Educational Institute of Epirus (GR)
Katerina D. Tzimourta's picture
Katerina D. Tzimourta
Loukas G. Astrakas's picture
Loukas G. Astrakas
Spyridon Konitsiotis's picture
Spyridon Konitsiotis
An Intermixed Color Paradigm for P300 Spellers: A Comparison with Gray-scale Spellers
P300 speller systems represent one of the most basic applications of Brain-Computer Interfaces (BCIs). A traditional P300 speller consists of a 6 by 6 grid of characters in which each column or row in this grid intensifies at random. During such intensification process, the electroencephalography (EEG) data of the subject is recorded and analyzed to determine the character to be spelled.
Mina Meshriky's picture
Mina Meshriky
Ain Shams University (EG)
Seif Eldawlatly's picture
Seif Eldawlatly
Gamal Aly's picture
Gamal Aly
Simulation of Spiral Waves and Point Sources in Atrial Fibrillation with Application to Rotor Localization
Cardiac simulations play an important role in studies involving understanding and investigating the mechanisms of cardiac arrhythmias. Today, studies of arrhythmogenesis and maintenance are largely being performed by creating simulations of a particular arrhythmia with high accuracy comparable to the results of clinical experiments. Atrial fibrillation (AF), the most common arrhythmia in the United States and many other parts of the world, is one of the major field where simulation and modeling is largely used.
Prasanth Ganesan's picture
Prasanth Ganesan
Kristina Shillieto's picture
Kristina Shillieto
Behnaz Ghoraani's picture
Behnaz Ghoraani
Florida Atlantic University (USA)