scholarly journals Early-Stage Pilot Study on Using Fractional-Order Calculus-Based Filtering for the Purpose of Analysis of Electroencephalography Signals

2016 ◽  
Vol 47 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Aleksandra Kawala-Janik ◽  
Waldemar Bauer ◽  
Magda Żołubak ◽  
Jerzy Baranowski
Keyword(s):  
Pilot Study ◽  
Fractional Order ◽  
Early Stage ◽  
Rapid Development ◽  
Eeg Signals ◽  
Fractional Order Calculus ◽  
Non Invasive ◽  
Research Areas ◽  
Fractional Filters ◽  
The Brain

Abstract Analysis of Electroencephalography (EEG) signals has recently awoken the increased interest of numerous researchers all around the world with regard to rapid development of Brain-Computer Interaction-related research areas and because EEG signals are implemented in most of the non-invasive BCI systems, as they provide necessary information regarding activity of the brain. In this paper, a very early stage pilot study on implementation of filtering based on fractional-order calculus (Bi-Fractional Filters – BFF) for the purpose of EEG signal classification is presented in brief.

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

scholarly journals Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2860 ◽  
Author(s):  
Daniel Schmidt ◽  
Javier Villalba Diez ◽  
Joaquín Ordieres-Meré ◽  
Roman Gevers ◽  
Joerg Schwiep ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Improvement ◽  
Balanced Scorecard ◽  
Industry 4.0 ◽  
Transformation Process ◽  
Eeg Signals ◽  
Advantages And Disadvantages ◽  
Non Invasive ◽  
Recorded Data ◽  
The Brain

Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the “HOSHIN KANRI TREE” (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain’s activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders.

scholarly journals Analysing EEG signals for mathematical computations

2019 ◽  
Vol 292 ◽  
pp. 01043
Author(s):  
Martin Strmiska ◽  
Zuzana Koudelkova
Keyword(s):  
Brain Activity ◽  
Gaussian Elimination ◽  
Practical Method ◽  
System Of Equations ◽  
Eeg Signals ◽  
Mathematical Methods ◽  
Scanning Method ◽  
Non Invasive ◽  
Solving Equations ◽  
The Brain

Brain computer interface (BCI) is a device that allows us to scan brainwaves. Achieved signals can be processed using a computer and the analyzed brain activity can be than monitored. In this paper, the use of the non-invasive brain scanning method applied on person at solving a system of equations is described. This solving the system of equations was obtained by two mathematical methods. The measurement was performed for solving equations by Gaussian elimination and by substitution methods separately. The results of the measurements were visualized by graphing the brain activity. The aim of the work is to determine the more practical method of those two.

A pilot study to compare FXR expression in normal and malignant tissue in receptor-positive early-stage breast cancer

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e14645-e14645
Author(s):  
N. Kounalakis ◽  
S. Lau ◽  
D. Darling ◽  
M. Palomares ◽  
M. Senthil ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Pilot Study ◽  
Normal Tissue ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Early Stage ◽  
Normal Breast ◽  
Invasive Cancer ◽  
Malignant Tissue ◽  
Non Invasive

e14645 Background: Farnesoid X receptor (FXR), a nuclear receptor, is a ligand dependent transcriptional factor regulating cholesterol and carbohydrate metabolism. Recently, FXR was shown to have a contributing role in colorectal cancer. We hypothesize that FXR expression changes from normal to premalignant to malignant tissue in patients with breast cancer. Methods: We identified 16 paired formaldehyde fixed, paraffin embedded tissue (normal, premalignant, and malignant) from patients with receptor positive, early stage breast cancer. Clinical information was extracted from a prospective database initiated in 2006 under institutional approval. Immunohistochemical staining of FXR using a validated polyclonal antibody was completed with appropriate positive and negative controls. The slides were graded independently by two investigators using an agreed upon scale to detect the percentage of positively stained cells to the nearest 10th percentile. Statistical analysis was performed by ANOVA and Student's t-test. A p-value of 0.05 was considered significant in all analyses. Results: Normal tissue and invasive cancer was identified in all 16 patient specimens. Of the 16 invasive cancers, 12 were ductal and 4 were lobular. 8/16 (50%) of the specimens also contained non-invasive cancer. 5/16 patients (31%) had N1 disease. FXR expression did not correlate with grade, histology, stage, or lymph node status. However, FXR expression increases with malignant transformation of the breast cancer cell. The mean percentage of cells staining positive for FXR in normal breast tissue was 58%, non-invasive 72% and invasive 79%. FXR staining in normal breast tissue was significantly less when compared to both invasive and noninvasive cancer (p< 0.007). Conclusions: FXR expression is upregulated in breast cancer when compared with expression in normal tissue and appears to progressively increase along the continuum of malignancy. Our pilot study results warrant further evaluation into FXR as a predictive biomarker for breast cancer, given the ability to target FXR via development of non-toxic oral ligands. No significant financial relationships to disclose.

Analysis Methods of EEG Signals: A Review in EEG Application for Brain Disorder

2015 ◽  
Vol 72 (2) ◽  
Author(s):  
Faridah Abd Rahman ◽  
Mohd Fauzi Othman ◽  
Nurul Aimi Shaharuddin
Keyword(s):  
Fourier Transform ◽  
Early Stage ◽  
Event Related Potential ◽  
Eeg Signal ◽  
Analysis Method ◽  
Eeg Signals ◽  
Brain Disorder ◽  
Detection Tool ◽  
Short Time ◽  
The Brain

The electroencephalograph (EEG) is a medical modality that plays crucial roles in detecting, displaying and recording electrical activity in the brain. This paper reviews the analysis method of EEG signal for common diseases in Malaysia which are autism, Cerebral Palsy (CP), Parkinson and schizophrenia from Malaysian and worldwide research paper that has been published. Fast Fourier Transform, Short Time Fourier Transform (STFT) and event-related potential (ERP) are some of the techniques used in analyzing EEG signal were discussed in this paper. It can be concluded that EEG plays its role as a detection tool to detect the disease in the early stage, rehabilitation, classification or as an assistive tool for the patient according to the needs of the diseases.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Non-Invasive Distinction of Non-Alcoholic Fatty Liver Disease using Urinary Volatile Organic Compound Analysis: Early Results

2015 ◽  
Vol 24 (2) ◽  
pp. 197-201 ◽  
Author(s):  
Ramesh P. Arasaradnam ◽  
Michael McFarlane ◽  
Emma Daulton ◽  
Erik Westenbrink ◽  
Nicola O’Connell ◽  
...  
Keyword(s):  
Liver Disease ◽  
Pilot Study ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Diagnostic Methods ◽  
Screening Methods ◽  
Alcoholic Fatty Liver ◽  
Non Invasive ◽  
Volatile Organic ◽  
Alcoholic Fatty Liver Disease

Background & Aims: Non-Alcoholic Fatty Liver Disease (NAFLD) is the commonest cause of chronic liver disease in the western world. Current diagnostic methods including Fibroscan have limitations, thus there is a need for more robust non-invasive screening methods. The gut microbiome is altered in several gastrointestinal and hepatic disorders resulting in altered, unique gut fermentation patterns, detectable by analysis of volatile organic compounds (VOCs) in urine, breath and faeces. We performed a proof of principle pilot study to determine if progressive fatty liver disease produced an altered urinary VOC pattern; specifically NAFLD and Non-Alcoholic Steatohepatitis (NASH).Methods: 34 patients were recruited: 8 NASH cirrhotics (NASH-C); 7 non-cirrhotic NASH; 4 NAFLD and 15 controls. Urine was collected and stored frozen. For assay, the samples were defrosted and aliquoted into vials, which were heated to 40±0.1°C and the headspace analyzed by FAIMS (Field Asymmetric Ion Mobility Spectroscopy). A previously used data processing pipeline employing a Random Forrest classification algorithm and using a 10 fold cross validation method was applied.Results: Urinary VOC results demonstrated sensitivity of 0.58 (0.33 - 0.88), but specificity of 0.93 (0.68 - 1.00) and an Area Under Curve (AUC) 0.73 (0.55 -0.90) to distinguish between liver disease and controls. However, NASH/NASH-C was separated from the NAFLD/controls with a sensitivity of 0.73 (0.45 - 0.92), specificity of 0.79 (0.54 - 0.94) and AUC of 0.79 (0.64 - 0.95), respectively.Conclusions: This pilot study suggests that urinary VOCs detection may offer the potential for early non-invasive characterisation of liver disease using 'smell prints' to distinguish between NASH and NAFLD.

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