Nonlinear Approach to Brain Signal Modeling

2011 ◽  
pp. 2834-2839
Author(s):  
Tugce Balli ◽  
Ramaswamy Palaniappan
Keyword(s):  
Signal Processing ◽  
Electrical Activity ◽  
Nonlinear Modeling ◽  
Biological Mechanisms ◽  
Linear Modeling ◽  
Memory Impairments ◽  
Biological Signal ◽  
Biological Signals ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

Biological signal is a common term used for time series measurements that are obtained from biological mechanisms and basically represent some form of energy produced by the biological mechanisms. Examples of such signals are electroencephalogram (EEG), which is the electrical activity of brain recorded by electrodes placed on the scalp; electrocardiogram (ECG), which is electrical activity of heart recorded from chest, and electromyogram (EMG), which is recorded from skin as electrical activity generated by skeletal muscles (Akay, 2000). Nowadays, biological signals such as EEG and ECG are analysed extensively for diagnosing conditions like cardiac arrhythmias in the case of ECG and epilepsy, memory impairments, and sleep disorders in case of EEG. Apart from clinical diagnostic purposes, in recent years there have been many developments for utilising EEG for brain computer interface (BCI) designs (Vaughan & Wolpaw, 2006). The field of signal processing provides many methods for analysis of biological signals. One of the most important steps in biological signal processing is the extraction of features from the signals. The assessment of such information can give further insights to the functioning of the biological system. The selection of proper methods and algorithms for feature extraction (i.e., linear/nonlinear methods) are current challenges in the design and application of real time biological signal analysis systems. Traditionally, linear methods are used for the analysis of biological signals (mostly in analysis of EEG). Although the conventional linear analysis methods simplify the implementation, they can only give an approximation to the underlying properties of the signal when the signal is in fact nonlinear. Because of this, there has been an increasing interest for utilising nonlinear analysis techniques in order to obtain a better characterisation of the biological signals. This chapter will lay the backgrounds to linear and nonlinear modeling of EEG signals, and propose a novel nonlinear model based on exponential autoregressive (EAR) process, which proves to be superior to conventional linear modeling techniques.

scholarly journals BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Carmen Vidaurre ◽  
Tilmann H. Sander ◽  
Alois Schlögl
Keyword(s):  
Signal Processing ◽  
Open Source ◽  
Open Source Software ◽  
Biomedical Signal Processing ◽  
Software Library ◽  
Biomedical Signal ◽  
Computer Interfaces ◽  
Relevant Element ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

Current Practices in Electroencephalogram- Based Brain-Computer Interfaces

2011 ◽  
pp. 888-901 ◽  
Author(s):  
Ramaswamy Palaniappan ◽  
Chanan S. Syan ◽  
Raveendran Paramesran
Keyword(s):  
Electrical Activity ◽  
Communication Systems ◽  
External Environment ◽  
Assistive Technologies ◽  
Memory Impairments ◽  
Menu Selection ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Electroencephalogram Eeg ◽  
The Brain

Electroencephalogram (EEG) is the electrical activity of the brain recorded by electrodes placed on the scalp. EEG signals are generally investigated for the diagnosis of mental conditions such as epilepsy, memory impairments, and sleep disorders. In recent years there has been another application using EEG: for brain-computer interface (BCI) designs (Vaughan & Wolpaw, 2006). EEG-based BCI designs are very useful for hands-off device control and communication as they use the electrical activity of the brain to interface with the external environment, therefore circumventing the use of peripheral muscles and limbs. Some current applications of BCIs in communication systems are for paralyzed individuals to communicate with their surroundings through character/menu selection and in device control such as wheelchair movement, prosthetics control, and flight and rehabilitative (assistive) technologies. For the general public, some of the possible applications are hands-off menu selection, flight/space control, and virtual reality (entertainment). BCI has also been applied in biometrics (Palaniappan & Mandic, 2007).

Polysomnography

2017 ◽  
Author(s):  
Nic Butkov
Keyword(s):  
Signal Processing ◽  
Digital Data ◽  
Electrode Placement ◽  
Electrical Safety ◽  
Preventative Measures ◽  
Recording Process ◽  
Patient Preparation ◽  
Sleep Recording ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitoring. This chapter also includes record samples of the various types of recording artifacts commonly found in sleep studies, with a detailed description of their causes, preventative measures, and recommended corrective actions.

scholarly journals Analysing electrocardiographic traits and predicting cardiac risk in UK biobank

2021 ◽  
Vol 10 ◽  
pp. 204800402110236
Author(s):  
Julia Ramírez ◽  
Stefan van Duijvenboden ◽  
William J Young ◽  
Michele Orini ◽  
Aled R Jones ◽  
...  
Keyword(s):  
Genetic Information ◽  
Association Studies ◽  
Cardiac Risk ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Biological Mechanisms ◽  
Clinical Tool ◽  
Genome Wide ◽  
Response To Exercise ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) is a commonly used clinical tool that reflects cardiac excitability and disease. Many parameters are can be measured and with the improvement of methodology can now be quantified in an automated fashion, with accuracy and at scale. Furthermore, these measurements can be heritable and thus genome wide association studies inform the underpinning biological mechanisms. In this review we describe how we have used the resources in UK Biobank to undertake such work. In particular, we focus on a substudy uniquely describing the response to exercise performed at scale with accompanying genetic information.

scholarly journals Biological Signal Processing with a Genetic Toggle Switch

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e68345 ◽  
Author(s):  
Patrick Hillenbrand ◽  
Georg Fritz ◽  
Ulrich Gerland
Keyword(s):  
Signal Processing ◽  
Toggle Switch ◽  
Biological Signal ◽  
Biological Signal Processing

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

scholarly journals Распознавание движений руки по сигналам электроэнцефалограммы на основе флуктуационного анализа

2019 ◽  
Vol 45 (4) ◽  
pp. 8 ◽  
Author(s):  
А.Н. Павлов ◽  
А.Е. Руннова ◽  
В.А. Максименко ◽  
О.Н. Павлова ◽  
Д.С. Гришина ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Mental Representation ◽  
Digital Signal ◽  
Fluctuation Analysis ◽  
Human Arm ◽  
Electroencephalogram Eeg

AbstractWe consider the task of recognition of fragments of multichannel electroencephalogram (EEG) records corresponding to motions of the human arm and to mental representation of these motions. It is shown that the problem of recognition can be solved by processing short EEG segments by the method of fluctuation analysis. The obtained results suggest that fluctuation analysis can be used as an algorithm of the digital signal processing in development of the neurointerface software.

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