Investigating the informative brain region in multiclass electroencephalography and near infrared spectroscopy based BCI system using band power based features.

In recent years, various brain imaging techniques have been used as input signals for brain-computer interface (BCI) systems. Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are two prominent techniques in this field, each with its own advantages and limitations. As a result, there is a growing tendency to integrate these methods in a hybrid within BCI systems.

Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets.

Excitation with one photon of a singlet fission (SF) material generates two triplet excitons, thus doubling the solar cell efficiency. Therefore, the SF molecules are regarded as new generation organic photovoltaics, but it is hard to identify them. Recently, it was demonstrated that molecules of low-to-intermediate diradical character (DRC) are potential SF chromophores.

An efficient 3D column-only P300 speller paradigm utilizing few numbers of electrodes and flashings for practical BCI implementation.

The event related P300 potentials, positive waveforms in electroencephalography (EEG) signals, are often utilized in brain computer interfaces (BCI). Many studies have been carried out to improve the performance of P300 speller systems either by developing signal processing algorithms and classifiers with different architectures or by designing new paradigms. In this study, a new paradigm is proposed for this purpose.

Investigating the effects of stimulus duration and inter-stimulus interval parameters on P300 based BCI application performance.

The main goal of electroencephalography (EEG) based brain-computer interface (BCI) research is to develop a fast and higher classification accuracy (CA) rate method than those of existing ones. Generally, in BCI applications, either motor imagery or event-related P300 based techniques are used for data recording. The stimulus duration (SD) and the inter-stimulus interval (ISI) are crucial two parameters directly affecting the decision speed of the BCI system.

Determination of hypertension disease using chirp z-transform and statistical features of optimal band-pass filtered short-time photoplethysmography signals.

Hypertension is the condition where the normal blood pressure is high. This situation is manifested by the high pressure of the blood in the vein towards the vessel wall. Hypertension mostly affects the brain, kidneys, eyes, arteries and heart.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites.

Investigation of neutrophil/lymphocyte ratio and mean platelet volume in patients diagnosed with preterm labor.

Background: To investigate the level of neutrophil/lymphocyte ratio (NLO) and mean platelet volume (MPV) in preterm birth in patients who gave birth before 37 weeks.

Method: This study was conducted by a retrospective examination of the patients who gave birth with preterm labor diagnosis from January 2017 to May 2018 at Ankara Keçiören Training and Research Hospital, Obstetrics and Gynecology Clinic. The study included 138 patients.

A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals.

Brain computer interface systems decode brain activities from electroencephalogram (EEG) signals and translate the user's intentions into commands to control and/or communicate with augmentative or assistive devices without activating any muscle or peripheral nerve. In this paper, we aimed to improve the accuracy of these systems using improved EEG signal processing techniques through a novel evolutionary approach (fusion-based preprocessing method). This approach was inspired by chromosomal crossover, which is the transfer of genetic material between homologous chromosomes.

The Effects of Minocycline on the Hippocampus in Lithium- Pilocarpine Induced Status Epilepticus in Rat: Relations with Microglial/Astrocytic Activation and Serum S100B Level.

Aim: To investigate possible correlations between serum S100B levels and microglial/astrocytic activation in status epilepticus (SE) in lithium-pilocarpine-exposed rat hippocampi and whether serum S100B levels linearly reflect neuroinflammation. Additionally, to assess the effects of minocycline (M), an inhibitor of neuroinflammation.

Material And Methods: Rats were divided into 4 groups (6/group), namely, control (C), sham, SE, and SE+M.

A robust and subject-specific sequential forward search method for effective channel selection in brain computer interfaces.

Background: The input signals of electroencephalography (EEG) based brain computer interfaces (BCI) are extensively acquired from scalp with a multi-channel system. However, multi-channel signals might contain redundant information and increase computational complexity. Furthermore, using only effective channels, rather than all channels, may enhance the performance of the BCI in terms of classification accuracy (CA).

Could Platelet Indices and Neutrophil to Lymphocyte Ratio Be New Biomarkers for Differentiation of Arteritic Anterior Ischemic Neuropathy from Non-Arteritic Type?

The aim of this study was to assess the possible relationship between AAION (arteritic anterior ischemic optic neuropathy) and NAION (non-arteritic anterior ischemic optic neuropathy) with blood platelet parameters and NLR (neutrophil-to-lymphocyte ratio). The medical records of 12 patients with AAION, 33 patients with NAION, and 35 healthy subjects were examined. MPV, PDW, and PCT values showed marked elevation in AAION and NAION groups compared with control group.

Olfactory Recognition Based on EEG Gamma-Band Activity.

There are various kinds of brain monitoring techniques, including local field potential, near-infrared spectroscopy, magnetic resonance imaging (MRI), positron emission tomography, functional MRI, electroencephalography (EEG), and magnetoencephalography. Among those techniques, EEG is the most widely used one due to its portability, low setup cost, and noninvasiveness. Apart from other advantages, EEG signals also help to evaluate the ability of the smelling organ.

Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery.

Background: Input signals of an EEG based brain computer interface (BCI) system are naturally non-stationary, have poor signal to noise ratio, depend on physical or mental tasks and are contaminated with various artifacts such as external electromagnetic waves, electromyogram and electrooculogram. All these disadvantages have motivated researchers to substantially improve speed and accuracy of all components of the communication system between brain and a BCI output device.

New Method: In this study, a fast and accurate decision tree structure based classification method was proposed for classifying EEG data to up/down/right/left computer cursor movement imagery EEG data.