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An Introduction To The Event Related Potential Technique

Functional connectivity of major depression disorder using ongoing EEG during music perception

BACKGROUND About one-third of the adult population is estimated to experience insomnia, which can have a detrimental effect on the quality of life of those affected. Insomnia is characterized by difficulty in initiating and/or maintaining sleep or experiencing nonrestorative sleep, which can lead to increased risk of accidents, higher work absenteeism, and the development of other conditions such as depression and anxiety. Currently, management of insomnia includes the use of pharmacological, psychological, and behavioral interventions as well as a range of complementary and alternative therapies. Pharmacotherapy, although commonly used, is often accompanied with unwanted physiological and psychological side effects, such as daytime sleepiness, which can affect the ability of individuals to perform daily activities. Alternatives to pharmacological interventions include, for example, education in sleep hygiene, cognitive behavioral therapy, neurofeedback brain training, and bright light therapy. Music is frequently used by individuals as a self-help strategy for the management of insomnia. It is thought that listening to slow relaxing music has an effect on the neuroendocrine and sympathetic nervous systems that leads to a reduction in heart rate, blood pressure, and respiratory rate, therefore inducing a state of relaxation. Music has also been linked to a reduction in circulating noradrenaline, which is involved in the onset of sleep.

Music for Insomnia in Adults.

Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition.

Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate the diagnosis and treatment of epilepsy. Firstly, wavelet transforms are applied to EEG recordings for filtering pre-processing. Then the relative energies of signals in several particular frequency bands are calculated and inputted into Bi-GRU network. Afterwards, the outputs of Bi-GRU network are further processed by moving average filtering, threshold comparison and seizure merging to generate the discriminant results that the tested EEG belong to seizure or not. Evaluated on CHB-MIT scalp EEG database, the proposed seizure detection method obtained an average sensitivity of 93.89% and an average specificity of 98.49%. 124 out of 128 seizures were correctly detected and the achieved average false detection rate was 0.31 per hour on 867.14 h testing data. The results show the superiority of Bi-GRU network in seizure detection and the proposed detection method has a promising potential in the monitoring of long-term EEG. 

/pdf/epileptic-seizure-detection-based-on-bidirectional-gated-lu70h4qo.pdf

Epileptic Seizure Detection Based on Bidirectional Gated Recurrent Unit Network

One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG

Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Neural oscillations exert a key role in coordinating the activity of remote brain regions, and various assemblies of oscillations can modulate different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of electroencephalography (EEG) oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time $\times $ frequency $\times $ connectivity $\times $ subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillatory networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

/pdf/identifying-oscillatory-hyperconnectivity-and-3du3yodn3d.pdf

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

Coupled Nonnegative Matrix/Tensor Factorization in Brain Imaging Data

Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect different brain regions, and various assemblies of oscillations can form different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of EEG oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillation-modulated networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

/pdf/identifying-oscillatory-hyperconnectivity-and-3txw00lfke.pdf

Xiulin Wang

Papers

One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG

Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition.

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

Coupled Nonnegative Matrix/Tensor Factorization in Brain Imaging Data

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition