20 research outputs found
Classification of Epileptic EEG Signals by Wavelet based CFC
Electroencephalogram, an influential equipment for analyzing humans
activities and recognition of seizure attacks can play a crucial role in
designing accurate systems which can distinguish ictal seizures from regular
brain alertness, since it is the first step towards accomplishing a high
accuracy computer aided diagnosis system (CAD). In this article a novel
approach for classification of ictal signals with wavelet based cross frequency
coupling (CFC) is suggested. After extracting features by wavelet based CFC,
optimal features have been selected by t-test and quadratic discriminant
analysis (QDA) have completed the Classification.Comment: Electroencephalogram; Wavelet Decomposition; Cross Frequency
Coupling;Quadratic Discriminant Analysis; T-test Feature Selectio
Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings
Brain Electrical Stimulation for Animal Navigation
The brain stimulation and its widespread use is one of the most important
subjects in studies of neurophysiology. In brain electrical stimulation
methods, following the surgery and electrode implantation, electrodes send
electrical impulses to the specific targets in the brain. The use of this
stimulation method is provided therapeutic benefits for treatment chronic pain,
essential tremor, Parkinsons disease, major depression, and neurological
movement disorder syndrome (dystonia). One area in which advancements have been
recently made is in controlling the movement and navigation of animals in a
specific pathway. It is important to identify brain targets in order to
stimulate appropriate brain regions for all the applications listed above. An
animal navigation system based on brain electrical stimulation is used to
develop new behavioral models for the aim of creating a platform for
interacting with the animal nervous system in the spatial learning task. In the
context of animal navigation the electrical stimulation has been used either as
creating virtual sensation for movement guidance or virtual reward for movement
motivation. In this paper, different approaches and techniques of brain
electrical stimulation for this application has been reviewed.
Keywords: Rat Robot, Brain Computer Interface, Electrical Stimulation, Cyborg
Intelligence, Brain to Brain InterfaceComment: in Fars
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe
Methods for improving the quality and sorting of intracortical recordings for brain-machine interfacing
Methods for improving the quality and sorting of intracortical recordings for brain-machine interfacing
Unsupervised wavelet optimization for detection of intra-cortical action potentials with low signal-to-noise ratio
Unsupervised wavelet optimization for detection and clustering of intra-cortical action potentials
A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment
Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals.</p