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. The primary aim of this study is to identify highly functional brain regions within an EEG + NIRS-based BCI system. To achieve this, the research focused on identifying EEG electrodes positioned in different brain lobes and then investigating the functionality of each lobe. The methodology involved segmenting the EEG + NIRS dataset into 2.4 s time windows, and then extracting band-power based features from these segmented signals. A classification algorithm, specifically the k-nearest neighbor algorithm, was then used to classify the features. The result was a remarkable classification accuracy (CA) of 95.54%±1.31 when using the active brain region within the hybrid model. These results underline the effectiveness of the proposed approach, as it outperformed both standalone EEG and NIRS modalities in terms of CA by 5.19% and 40.90%, respectively. Furthermore, the results confirm the considerable potential of the method in classifying EEG + NIRS signals recorded during tasks such as reading text while scrolling in different directions, including right, left, up and down. This research heralds a promising step towards enhancing the capabilities of BCI systems by harnessing the synergistic power of EEG and NIRS technologies.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

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. This prompts a low-cost strategy for finding new SF candidates by computational high-throughput workflows. We propose a machine learning aided screening for SF entrants based on their DRC. Our data set comprises 469 784 compounds extracted from the PubChem database, structurally rich but inherently imbalanced regarding DRC values. We developed well performing classification models that can retrieve potential SF chromophores. The latter (∼4%) were analyzed by K-means clustering to reveal qualitative structure-property relationships and to extract strategies for molecular design. The developed screening procedure and data set can be easily adapted for applications of diradicaloids in photonics and spintronics.

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. The proposed paradigm combines two remarkable properties of being a 3D animation and utilizing column-only flashings as opposed to classical paradigms which are based on row-column flashings in 2D manner. The new paradigm is utilized in a traditional two-layer artificial neural networks model with a single output neuron, and numerous experiments are conducted to evaluate and compare the performance of the proposed paradigm with that of the classical approach. The experimental results, including statistical significance tests, are presented for single and multiple EEG electrode usage combinations in 1, 3 and 15 flashing repetitions to detect P300 waves as well as to recognize target characters. Using the proposed paradigm, the best average classification accuracy rates on the test data are improved from 89.97% to 93.90% (an improvement of 4.36%) for 1 flashing, from 97.11% to 98.10% (an improvement of 1.01%) for 3 flashings and from 99.70% to 99.81% (an improvement of 0.11%) for 15 flashings when all electrodes, included in the study, are utilized. On the other hand, the accuracy rates are improved by 9.69% for 1 flashing, 4.72% for 3 flashings and 1.73% for 15 flashings when the proposed paradigm is utilized with a single EEG electrode (P8). It is observed that the proposed speller paradigm is especially useful in BCI systems designed for few EEG electrodes usage, and hence, it is more suitable for practical implementations. Moreover, all participants, given a subjective test, declared that the proposed paradigm is more user-friendly than classical ones.

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. In this study, we investigated the performance of the P300 based application in terms of speed and CA for three kinds of protocols which are called fast, medium, and slow included different SD and the ISI values. The training and test data sets were recorded in one week of delay from 8 subjects. The features were extracted by standard deviation, variance, mean, Wavelet Transform and Fourier Transform techniques. Afterwards, they were classified by the k-nearest neighbor algorithm. We obtained 87.08%, 85.41% and 83.95% average CA rate for the fast, medium, and slow protocols, respectively. The obtained results showed that the proposed fast protocol method achieved CA rate between 78.33% and 93.33%. Based on the obtained results, it can be concluded that the fast protocol values can be used for establishing a more accurate and faster P300 based BCI.

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. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

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. Patients were divided into three groups: Early Preterm (delivery before 34 weeks, Group I = 39), Late Preterm (delivery between 34 and 37 weeks, Group II = 59) and the Control Group (delivery after 37 weeks, Group III = 40). All three groups were compared with respect to demographic, obstetric and laboratory results, MPV and NLO parameters. RESULTS: The difference between the groups was not significant when the patients were compared in terms of age, gravida, parity, fetal sex and smoking. When the three groups were compared in terms of leukocyte, neutrophil, lymphocyte, hemoglobin, MPV and NLO, NLO was higher and MPV rate was lower in the preterm birth group, which was significant (p < 0.05). When the preterm delivery group was further divided as early preterm (<34 weeks) and late (34-37 weeks) preterm delivery group, the NLO rate was higher in the former group, while MPV was lower and the difference was significant (p < 0.05). When the patients were compared in terms of caesarean and vaginal delivery, 58.6% (81) of the total patients were delivered vaginally and 41.4% (57) were delivered by caesarean section. The most common cesarean indication was a previous caesarean section history. Subsequent indications included breech presentation, fetal distress, oligohydramnios, cephalo-pelvic disproportion (CPD), and placenta previa, respectively. The C-section rate was higher in the preterm group when the groups were compared in terms of the mode of delivery, and the difference between them was significant (p < 0.05). CONCLUSION: NLO and MPV may be decisive as a proinflammatory process marker in patients who give birth before 37 weeks. Preterm births and fetuses of pregnant women with high NLO and low MPV may be considered to be likely to go to the neonatal care unit.

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. In this study, the proposed fusion-based preprocessing method was applied to an open access dataset collected from 29 subjects. Then, features were extracted by the autoregressive model and classified by k-nearest neighbor classifier. We achieved classification accuracy (CA) ranging from 67.57 to 99.70% for the detection of binary mental arithmetic (MA) based EEG signals. In addition to obtaining an average CA of 88.71%, 93.10% of the subjects showed performance improvement using the fusion-based preprocessing method. Furthermore, we compared the proposed study with the common average reference (CAR) method and without applying any preprocessing method. The achieved results showed that the proposed method provided 3.91% and 2.75% better CA then the CAR and without applying any preprocessing method, respectively. The results also prove that the proposed evolutionary preprocessing approach has great potential to classify the EEG signals recorded during MA task.

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. Therefore, the diagnosis of this common disease is important. It may take days, weeks or even months for diagnosis. Often a device, called a blood pressure holter, is connected to the person for 24 or 48 h and the person's blood pressure is recorded at certain intervals. Diagnosis can be made by the specialist physician considering these results. In recent years, various physiological measurement techniques have been used to accelerate this time-consuming diagnostic phase and intelligent models have been proposed. One of these techniques is photopletesmography (PPG). In this study, a model for the detection of hypertension disease in individuals was proposed using chirp z-transform and statistical features (total band power, autoregressive model parameters, standard deviation of signal's derivative and zero crossing rate) of optimal band-pass filtered short-time PPG signals. The proposed method was successfully applied to 657 PPG trials, which each of them had only 2.1 s signal length and achieved a classification accuracy rate of 77.52% on the test data. The results showed that the diagnosis of hypertension can be performed effectively by chirp z-transform and statistical features and support vector machine classifier using optimal frequency range of 1.4-6 Hz.

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. Animals were exposed to lithium-pilocarpine to induce SE in the SE and SE+M groups. Cardiac blood was collected to measure S100B levels, and coronal brain sections including the hippocampus were prepared to examine microglial/astrocytic activation and to evaluate neuroinflammation at day 7 of SE. RESULTS: Serum S100B levels, OX42 (+) microglia in CA1, and GFAP (+) astrocytes in both CA1 and dentate gyrus (DG) were higher in the SE+M group than in the C group. Most importantly, highly positive correlations were found between S100B levels and microglial activation in CA1, apart from astrocytic activation in CA1 and DG. Unexpectedly, microglial activation in CA1 and astrocytic activation in DG were also enhanced in the SE+M group compared with the C group. Moreover, M administration reversed the neuronal loss observed in DG during SE. CONCLUSION: These results suggest that serum S100B is a candidate biomarker for monitoring neuroinflammation and that it may also help predict diagnosis and prognosis.

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). NEW METHOD: We proposed a robust and subject-specific sequential forward search method (RSS-SFSM) for effective channel selection (ECS). The ECS procedure executes a sequential search among each of the candidate channels in order to find the channels which maximize the CA performance of the validation set. It should be noted that in order to avoid the problems of random selections in the validation set, we applied the ECS procedure for 100 times. Then, the total numbers of the selection of each channel present the effective ones. To demonstrate its reliability and robustness, the proposed method was applied to two data sets. RESULTS: The achieved results showed that the proposed method not only improved the average CA by 15.98%, but also decreased the considered number of channels and computational complexity by 71.53% on average. COMPARISON WITH EXISTING METHOD(S): Compared with the existing methods, we achieved better results in terms of both the classification accuracy improvement and channel reduction rates. CONCLUSIONS: Features extracted by Hilbert transform and sum derivative methods were effectively classified by support vector machine. In conclusion, the results obtained proved that the RSS-SFSM shows great potential for determining effective channel(s).

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. The mean NLR was statistically significantly higher only in AAION group compared to the NAION and control groups, suggesting that platelet function plays an important role in AIONs and NLR might be used to differentiate AAION from NAION.

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. In such studies, EEG signals, which are recorded during smelling, are analyzed to determine the subject lacks any smelling ability or to measure the response of the brain. The main idea of this study is to show the emotional difference in EEG signals during perception of valerian, lotus flower, cheese, and rosewater odors by the EEG gamma wave. The proposed method was applied to the EEG signals, which were taken from five healthy subjects in the conditions of eyes open and eyes closed at the Swiss Federal Institute of Technology. In order to represent the signals, we extracted features from the gamma band of the EEG trials by continuous wavelet transform with the selection of Morlet as a wavelet function. Then the [Formula: see text]-nearest neighbor algorithm was implemented as the classifier for recognizing the EEG trials as valerian, lotus flower, cheese, and rosewater. We achieved an average classification accuracy rate of 87.50% with the 4.3 standard deviation value for the subjects in eyes-open condition and an average classification accuracy rate of 94.12% with the 2.9 standard deviation value for the subjects in eyes-closed condition. The results prove that the proposed continuous wavelet transform-based feature extraction method has great potential to classify the EEG signals recorded during smelling of the present odors. It has been also established that gamma-band activity of the brain is highly associated with olfaction.

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. The data sets were acquired from three healthy human subjects in age group of between 24 and 29 years old in two sessions on different days. RESULTS: The proposed decision tree structure based method was successfully applied to the present data sets and achieved 55.92%, 57.90% and 82.24% classification accuracy rate on the test data of three subjects. COMPARISON WITH EXISTING METHOD(S): The results indicated that the proposed method provided 12.25% improvement over the best results of the most closely related studies although the EEG signals were collected on two different sessions with about 1 week interval. CONCLUSIONS: The proposed method required only a training set of the subject and automatically generated specific DTS for each new subject by determining the most appropriate feature set and classifier for each node. Additionally, with further developments of feature extraction and/or classification algorithms, any existing node can be easily replaced with new one without breaking the whole DTS. This attribute makes the proposed method flexible.