synchronization likelihood
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Author(s):  
Naishi Feng ◽  
Fo Hu ◽  
Hong Wang ◽  
Bin Zhou

Decoding brain intention from noninvasively measured neural signals has recently been a hot topic in brain-computer interface (BCI). The motor commands about the movements of fine parts can increase the degrees of freedom under control and be applied to external equipment without stimulus. In the decoding process, the classifier is one of the key factors, and the graph information of the EEG was ignored by most researchers. In this paper, a graph convolutional network (GCN) based on functional connectivity was proposed to decode the motor intention of four fine parts movements (shoulder, elbow, wrist, hand). First, event-related desynchronization was analyzed to reveal the differences between the four classes. Second, functional connectivity was constructed by using synchronization likelihood (SL), phase-locking value (PLV), H index (H), mutual information (MI), and weighted phase-lag index (WPLI) to acquire the electrode pairs with a difference. Subsequently, a GCN and convolutional neural networks (CNN) were performed based on functional topological structures and time points, respectively. The results demonstrated that the proposed method achieved a decoding accuracy of up to 92.81% in the four-class task. Besides, the combination of GCN and functional connectivity can promote the development of BCI.


Author(s):  
Ali Olamat ◽  
Pinar Ozel ◽  
Aydin Akan

Epilepsy is a persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analysis and statistical rescale range analysis. In this study, two different states of epilepsy EEG data (preictal and ictal phases), obtained from 17 subjects (18 channels each), were analyzed by a new method called state transfer network (STN). The analysis performed by STN yields a network metric called motifs, which are averaged over all channels and subjects in terms of their persistence level in the network. The results showed an increase of overall motif persistence during the ictal over the preictal phase, reflecting the synchronization increase during the seizure phase (ictal). An evaluation of intermotif cross-correlation indicated a definite manifestation of such synchronization. Moreover, these findings are compared with several other well-known methods such as synchronization likelihood (SL), visibility graph similarity (VGS), and global field synchronization (GFS). It is hinted that the STN method is in good agreement with approaches in the literature and more efficient. The most significant contribution of this research is introducing a novel nonlinear analysis technique of generalized synchronization. The STN method can be used for classifying epileptic seizures based on the synchronization changes between multichannel data.


Author(s):  
A. Rojas ◽  
E. Kroupi ◽  
D. Ibanez ◽  
J. Picardo ◽  
G. Garcia-Banda ◽  
...  

2020 ◽  
Vol 36 (1) ◽  
pp. 38-47
Author(s):  
Akiyoshi Akiyama ◽  
Jeng-Dau Tsai ◽  
Emily W. Y. Tam ◽  
Daphne Kamino ◽  
Cecil Hahn ◽  
...  

The purpose of this study is to investigate whether listening to music and white noise affects functional connectivity on scalp electroencephalography (EEG) in neonates in the neonatal intensive care unit. Nine neonates of ≥34 weeks’ gestational age, who were already undergoing clinical continuous EEG monitoring in the neonatal intensive care unit, listened to lullaby-like music and white noise for 1 hour each separated by a 2-hour interval of no intervention. EEG segments during periods of music, white noise, and no intervention were band-pass filtered as delta (0.5-4 Hz), theta (4-8 Hz), lower alpha (8-10 Hz), upper alpha (10-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz). Synchronization likelihood was used as a measure of connectivity between any 2 electrodes. In theta, lower alpha, and upper alpha frequency bands, the synchronization likelihood values yielded statistical significance with sound (music, white noise and no intervention) and with edge (between any 2 electrodes) factors. In theta, lower alpha, and upper alpha frequency bands, statistical significance was obtained between music and white noise ( t = 3.12, 3.32, and 3.68, respectively; P < .017), and between white noise and no intervention ( t = 4.51, 3.09, and 2.95, respectively, P < .017). However, there was no difference between music and no intervention. Although limited by a small sample size and the 1-time only auditory intervention, these preliminary results demonstrate the feasibility of EEG connectivity analyses even at bedside in neonates on continuous EEG monitoring in the neonatal intensive care unit. They also point to the possibility of detecting significant changes in functional connectivity related to the theta and alpha bands using auditory interventions.


2020 ◽  
Vol 30 (09) ◽  
pp. 2050046
Author(s):  
Pinar Ozel ◽  
Ali Karaca ◽  
Ali Olamat ◽  
Aydin Akan ◽  
Mehmet Akif Ozcoban ◽  
...  

Obsessive–compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.


2019 ◽  
Vol 51 (3) ◽  
pp. 146-154
Author(s):  
Ana Calzada-Reyes ◽  
Alfredo Alvarez-Amador ◽  
Lídice Galán-García ◽  
Mitchell Valdés-Sosa

Introduction. Functional brain differences related to sex in psychopathic behavior represent an important field of neuroscience research; there are few studies on this area, mainly in offender samples. Objective. The aim of this study was to investigate the presence of electrophysiological differences between male and female psychopath offenders; specifically, we wanted to assess whether the results in quantitative EEG, low-resolution electromagnetic tomography (LORETA), and changes in synchronous brain activity could be related to sex influence. Sample and Methods. The study included 31 male and 12 female psychopath offenders, according to the Hare Psychopathy Checklist–Revised criteria from 2 prisons located in Havana City. The EEG visual inspection characteristics and the use of frequency domain quantitative analysis techniques are described. Results. The resting EEG visual analyses revealed a high percentage of EEG abnormalities in both studied groups. Significant statistical differences between the mean parameters of cross spectral measures between psychopathic offender groups were found in the beta band at bilateral frontal derivation and centroparietal areas. LORETA showed differences especially in the paralimbic and parieto-occipital areas Synchronization likelihood revealed a significant group effect in the 26 to 30 Hz band. These results indicate that combining quantitative EEG, LORETA analysis, and synchronization likelihood may improve the neurofunctional differentiation between psychopath offenders of both sexes.


2018 ◽  
Vol 50 (2) ◽  
pp. 88-99 ◽  
Author(s):  
Bo Tan ◽  
Qingxiao Liu ◽  
Chaoyang Wan ◽  
Zhenlan Jin ◽  
Yanchun Yang ◽  
...  

Obsessive-compulsive disorder (OCD) is a common inheritable psychiatric disorder characteristic of repetitive thinking, imagination (obsession), and stereotyped behaviors (compulsive). To explore whether there is an alteration of brain functional connectivity (BFC) in patients with OCD during rest, electroencephalogram (EEG) data of healthy controls (HCs) and patients with OCD were collected during rest in both eyes-closed and eyes-open states. Synchronization likelihood and graph theory were applied to construct and analyze brain functional networks of patients with OCD and HCs. Patients with OCD showed abnormal graph-theoretic parameters and impaired small world features in the alpha and beta bands. In addition, the topological analysis consistently showed that the long-range BFC of alpha rhythm was significantly reduced in the bilateral posterior areas in patients with OCD in comparison with HCs, while the BFC in the beta rhythm was significantly increased only in the eyes-open state. The findings suggest that the BFC of patients with OCD show abnormal small-world features and altered topological structure during rest, mainly in alpha and beta bands, which may provide a new insight for the diagnosis and treatment of OCD.


2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Yan Chen ◽  
Xinyu Liu ◽  
Shan Li ◽  
Hong Wan

Recent studies indicate that the local field potential (LFP) carries information about an animal’s behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the k-nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 ± 8%) was significantly higher than that of the power features (61 ± 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.


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