Browsing by Subject "Electroencephalography"

Now showing 1 - 8 of 8

Open Access
BRAPH: A graph theory software for the analysis of brain connectivity
(Public Library of Science, 2017) Mijalkov, M.; Kakaei, E.; Pereira, J. B.; Westman, E.; Volpe, G.
The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH–BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimer’s disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinson’s patients with mild cognitive impairment. © 2017 Mijalkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Open Access
Dipole source reconstruction of brain signals by using particle swarm optimization
(IEEE, 2009) Alp, Yaşar Kemal; Arıkan, Orhan; Karakaş, S.
Resolving the sources of neural activity is of prime importance in the analysis of Event Related Potentials (ERP). These sources can be modeled as effective dipoles. Identifying the dipole parameters from the measured multichannel data is called the EEG inverse problem. In this work, we propose a new method for the solution of EEG inverse problem. Our method uses Particle Swarm Optimization (PSO) technique for optimally choosing the dipole parameters. Simulations on synthetic data sets show that our method well localizes the dipoles into their actual locations. In the real data sets, since the actual dipole parameters aren't known, the fit error between the measured data and the reconstructed data is minimized. It has been observed that our method reduces this error to the noise level by localizing only a few dipoles in the brain.
Open Access
EEG işareti üzerinde sara ataklarının ve sara belirtisi işaretlerin bulunması
(2008-04) Yücel, Zeynep; Özgüler, A. Bülent
Symptoms of epilepsy, which is characterized by abnormal brain electrical activity, can be observed on electroencephalography (EEG) signal. This paper employs models of chaotic measures of EEG and aims to help detection of epilepsy seizures and diagnosis of epileptic indicators in seizure-free signals. ©2008 IEEE.
Open Access
EEG sinyallerinde gamma tepkisinin tespiti
(IEEE, 2006-04) Tüfekçi, D. İlhan; Karakaş, S.; Arıkan, Orhan
In the detection of the existence of the early gamma response, subjective methods have been used. In this study, an automated gamma detection technique is developed based on the features obtained from the time - frequency representation of the EEG signal in the gamma frequency band. The technique easily discriminates the gamma response existing and non-existing cases for the generated synthetic data. The classification of the technique and that of the expert opinion coincide %77 for real EEG data. © 2006 IEEE.
Open Access
Extraction of sparse spatial filters using Oscillating Search
(IEEE, 2012) Onaran, İbrahim; İnce, N. Fırat; Abosch, A.; Çetin, A. Enis
Common Spatial Pattern algorithm (CSP) is widely used in Brain Machine Interface (BMI) technology to extract features from dense electrode recordings by using their weighted linear combination. However, the CSP algorithm, is sensitive to variations in channel placement and can easily overfit to the data when the number of training trials is insufficient. Construction of sparse spatial projections where a small subset of channels is used in feature extraction, can increase the stability and generalization capability of the CSP method. The existing 0 norm based sub-optimal greedy channel reduction methods are either too complex such as Backward Elimination (BE) which provided best classification accuracies or have lower accuracy rates such as Recursive Weight Elimination (RWE) and Forward Selection (FS) with reduced complexity. In this paper, we apply the Oscillating Search (OS) method which fuses all these greedy search techniques to sparsify the CSP filters. We applied this new technique on EEG dataset IVa of BCI competition III. Our results indicate that the OS method provides the lowest classification error rates with low cardinality levels where the complexity of the OS is around 20 times lower than the BE. © 2012 IEEE.
Open Access
İki durumlu bir beyin bilgisayar arayüzünde özellik çıkarımı ve sınıflandırma
(IEEE, 2017-10) Altındiş, Fatih; Yılmaz, B.
Beyin bilgisayar arayüzü (BBA) teknolojisi motor nöronlarının özelliğini kaybeden ve hareket kabiliyeti kısıtlanmış ALS ve felçli hastalar gibi birçok kişinin dış dünya ile iletişimini sağlamaya yönelik kullanılmaktadır. Bu çalışmada, Avusturya’daki Graz Üniversitesi’nde alınmış EEG veri seti kullanılarak gerçek zamanlı EEG işleme simülasyonu ile motor hayal etme sınıflandırılması amaçlanmıştır. Bu veri setinde sağ el ya da sol elin hareket ettirilme hayali esnasında 8 kişiden alınmış iki kanallı EEG sinyalleri bulunmaktadır. Her katılımcıdan 60 sağ ve 60 sol olmak üzere toplamda 120 adet yaklaşık 9 saniyelik motor hayal etme deneme sinyali kayıt edilmiştir. Bu sinyaller filtrelemeye tabi tutulmuştur. Yirmi dört, 32 ve 40 elemanlı özellik vektörü bant geçiren filtreler kullanarak elde edilen göreceli güç değişim değerleridir (GGDD). Bu çalışmada, lineer diskriminant analizi (LDA), k en yakın komşular (KNN) ve destek vektör makinaları (SVM) ile sınıflandırma yapılmış, en iyi sınıflandırma performansının 24 değerli özellik vektörüyle ve LDA sınıflandırma yöntemiyle elde edildiği gösterilmiştir.
Open Access
Neural correlates of acquired color category effects
(2012) Clifford, A.; Franklin, A.; Holmes, A.; Drivonikou V.G.; Özgen, E.; Davies I.R.L.
Category training can induce category effects, whereby color discrimination of stimuli spanning a newly learned category boundary is enhanced relative to equivalently spaced stimuli from within the newly learned category (e.g., categorical perception). However, the underlying mechanisms of these acquired category effects are not fully understood. In the current study, Event-Related Potentials (ERPs) were recorded during a visual oddball task where standard and deviant colored stimuli from the same or different novel categories were presented. ERPs were recorded for a test group who were trained on these novel categories, and for an untrained control group. Category effects were only found for the test group on the trained region of color space, and only occurred during post-perceptual stages of processing. These findings provide new evidence for the involvement of cognitive mechanisms in acquired category effects and suggest that category effects of this kind can exist independent of early perceptual processes. © 2012.
Open Access
Seizure detection using least eeg channels by deep convolutional neural network
(Institute of Electrical and Electronics Engineers Inc., 2019) Avcu, Mustafa Talha; Zhang, Z.; Chan, D. W. S.
This work aims to develop an end-to-end solution for seizure onset detection. We design the SeizNet, a Convolutional Neural Network for seizure detection. To compare SeizNet with traditional machine learning approach, a baseline classifier is implemented using spectrum band power features with Support Vector Machines (BPsvm). We explore the possibility to use the least number of channels for accurate seizure detection by evaluating SeizNet and BPsvm approaches using all channels and two channels settings respectively. EEG Data is acquired from 29 pediatric patients admitted to KK Woman's and Children's Hospital who were diagnosed as typical absence seizures. We conduct leave-one-out cross validation for all subjects. Using full channel data, BPsvm yields a sensitivity of 86.6% and 0.84 false alarm (per hour) while SeizNet yields overall sensitivity of 95.8 % with 0.17 false alarm. More interestingly, two channels seizNet outperforms full channel BPsvm with a sensitivity of 93.3% and 0.58 false alarm. We further investigate interpretability of SeizNet by decoding the filters learned along convolutional layers. Seizure-like characteristics can be clearly observed in the filters from third and forth convolutional layers.