A Comprehensive Analysis on EEG Signal Classification Using Advanced Computational Analysis

Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user\u27s neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement as recorded brain signals can be unreliable and vary in pattern throughout time. In the initial work, a novel classifier structure is proposed to classify different types of imaginary motions (left hand, right hand, and imagination of words starting with the same letter) across multiple sessions using an optimized set of electrodes for each user. The proposed technique uses raw brain signals obtained utilizing 32 electrodes and classifies the imaginary motions using Artificial Neural Networks (ANN). To enhance the classification rate and optimize the set of electrodes of each subject, a majority voting system combining a set of simple ANNs is used. This electrode optimization technique achieved classification accuracies of 69.83%, 94.04% and 84.56% respectively for the three subjects considered in this work. In the second work, the signal variations are studied in detail for a large EEG dataset. Using the Independent Component Analysis (ICA) with a dynamic threshold model, noise features were filtered. The data was classified to a high precision of more than 94% using artificial neural networks. A decreased variance in classification validated both, the effectiveness of the proposed dynamic threshold systems and the presence of higher concentrations of noise in data for specific subjects. Using this variance and classification accuracy, subjects were separated into two groups. The lower accuracy group was found to have an increased variance in classification. To confirm these results, a Kaiser windowing technique was used to compute the signal-to-noise ratio (SNR) for all subjects and a low SNR was obtained for all EEG signals pertaining to the group with the poor data classification. This work not only establishes a direct relationship between high signal variance, low SNR, and poor signal classification but also presents classification results that are significantly higher than the accuracies reported by prior studies for the same EEG user dataset

FT-IR PROFILE SCREENING OF BIOACTIVE CHEMICAL COMPONENTS IN AQUEOUS EXTRACT OF ABRUS PRECATORIUS LINN PLANT LEAF

Objective: The FT-IR profile screening is aimed to focus on the bioactive chemical components analysis of aqueous extract of Abrus precatorius Linn plant leafs. Methods: The profile screening for the bioactive chemical components analysis was performed with standard methods by using FT-IR spectral technique. Results: The aqueous extract of the leaf were screened for the various bioactive functional chemical components. The spectrum of FT-IR showed the presence different functional groups of chemical constituents such as alcohols, phenols, carboxylic acids, amide, aldehydes, ketones, alkanes, alkenes, aromatics, esters, ethers, aliphatic amines, aromatic amines, peptides, nitro compounds, sulphone, phosphonate, phosphoramide, phosphonic acid, phosphine, silane, amine oxides, aromatic substituted compounds, nitroso, sulphate ester and alkyl halides compounds, which showed 27 major characteristic bands of bioactive chemical components. Conclusion: The results confirm the fact that leaf of Abrus precatorius Linn plant possesses different bioactive functional chemical components and generated the FT-IR spectrum profile for the medicinally important plant

Phytochemicals and Antioxidant Activity Investigation of Butea monosperma Lam. Leaves Ethanol Extract

The aim of this study was to investigate phytochemicals and antioxidant activity of plant Butea monosperma Lam. leaves ethanol extract. The different extracts of this plant were reported the rich source contents of bioactive phytochemicals in the leaves and afford for various pharmacological activities. The ethanol extract of leaves was subjected to investigate phytochemicals and antioxidant activity by using DPPH in vitro system. The provided evidence of results concluded that the ethanol extract of Butea monosperma Lam. leaves are potential sources of natural bioactive phytochemicals and showed potent in vitro antioxidant activity with their IC50 value of 44.16 μg/ml. Therefore phytochemical investigation of plant leaves ethanol extract was noted various bioactive phytochemicals, which may serve as a potent source of natural antioxidants

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Distinctness of EEG Based Brain Signal Readings and Their Potential for Biometric Systems

A human brain consists of a large concentration of neurons which create minute currents while communicating with each other. During specific tasks such as closing and opening of eyes or movement of the tongue, these currents are recorded using an electroencephalogram (EEG) noninvasively through placing electrodes on the scalp of a person. There have been avid studies on signals acquired through the EEG to physically move objects using machines and prosthetics. In these studies, it has been stated that the brain signals have been distinct based on the subject (person) but there hasn’t been any research conducted to show that brain signals are truly distinct and can be considered as a strong biometric signature. The motivation for this study was drawn from contrastive results acquired for multiple subjects during a previous research conducted on finding the most effective electrodes for a brain-computer interface (BCI). This study will use the publicly available recorded, refined, and filtered data for nine subjects from an online database (BCI competition IV Ð Dataset 2a and 2b). The data consists of signals acquired from 22 electrodes placed at specific locations on the scalp and sampled at a rate of 256 Hz. The process of placing electrodes and recording data is performed during three separate sessions. Among the three sessions acquired from the same subject, a strong correlation coefficient is sought to proceed to the next stage. Here the correlation coefficient is the measure of similarity between two signals and the similarity can range from Ô0Õ to Ô1Õ. If and when a consistent correlation of 0.6 or more is acquired out of a maximum of Ô1Õ for the data in all three sessions, then the research will proceed to compare signal data from different subjects for a relatively lower correlation coefficient. Data acquired in preliminary simulations show a distinctive contrast between subjects and in this study the primary focus will be finding a strong similarity among the brain signals acquired for the same subject. So in conclusion each subject’s data will be correlated for the same task (eyes opened and eyes closed) across three separate sessions then the one subject’s data will be correlated with another and the findings will be presented in the symposium

SCREENING OF PHYTOCHEMICALS AND IN VITRO ANTIDIABETIC ACTIVITY OF BAUHINIA RACEMOSA LAM. LEAVES

Objective: In this study, the leaves of medicinal plant Bauhinia racemosa Lam. with different pharmacological activities were subjected to phytochemical screening and assessment of their in vitro inhibitory potential with porcine pancreatic α-amylase enzyme to treat and management of diabetes.Methods: Plant leaves were extracted sequentially with ethanol solvent. A modified 3,5-dinitrosalicylic acid method was adopted to screen α-amylase inhibition assay. The ethanol extract was analyzed qualitatively and gas chromatography–mass spectrometry analysis technique for the active phytoconstituents according to the standard protocols.Results: A phytochemical screening of leaves extract reveals the presence of carbohydrate, alkaloids, saponin, glycosides, steroids, tannins, flavonoids, triterpenoid, and phenolic compounds. The ethanol extract reported inhibition of α-amylase enzyme activity at IC50 value 61.72 ± 0.03 μg/mL and acarbose as a standard drug at IC50 value 28.07 ± 0.02 μg/mL.Conclusion: The results of the study indicate that B. racemosa Lam. leaves contain some of bioactive phytochemicals might to be exhibiting in vitro antidiabetic activity, which was leading to decreases the rate of starch digestion

Experimental approach for seeing through walls using Wi-Fi enabled software defined radio technology

Modern handheld target detection methods are typically restricted to line of sight (LOS) techniques. The design of a new method to detect moving targets through non-transparent surfaces could greatly aid the safety of hazardous military and government operations. In this paper, we develop through-wall virtual imaging using Wi-Fi enabled software defined radio to see moving objects and their relative locations. We use LabVIEW and NI Universal Software Radio Peripheral (NI USRP2921 radios with Ettus Research LP0965 directive antennas) devices to detect moving objects behind walls by sending and receiving a signal with respect to the USRP's location. Based on the signal-to-interference ratio of our signal (rather than the traditional signal-to-noise method), we could determine the target object behind the wall. The two major applications for this project are: detecting an active shooter that is standing on the other side of the wall and detecting abnormalities in the human body such as breast cancer with more sensitive antennas. Likewise, firefighters, law enforcement officers, and military men would find more practical purposes for the use of this system in their fields. We evaluate the proposed model using experimental results