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g.BSanalyze

Products < Signal analysis < Post-processing <

Multi-modal off-line biosignal analysis under MATLAB

g.BSanalyze, gtec's biosignal analysis software, is an interactive environment for multimodal biosignal data processing and analysis in the fields of clinical research and life sciences. The investigation of patterns and signal features of biosignals allows to observe noninvasively brain, heart- and muscle functions and disfunctions.

g.BSanalyze's graphical user interface includes functions for defining electrode montages, spatial or temporal filter designs, artifact treatment, quality control, spectral analysis, coherence, correlation, bandpower analysis, ERD/ERS analyses, visualization and data set classification. You can load and save your preferred processing steps as a script program and automatically process your data in g.BSanalyze batch mode.

g.BSanalyze's processing capabilities allow you to extract relevant features of your multimodal data and to define useful parameters for postprocessing. Use these parameters directly with g.BSanalyze's classification tools to assign distinct classes to your data.

The combination of the graphical user interface and the programming environment makes g.BSanalyze an unique package for biosignal analyses.

Highlights

interactive and intuitive graphical user interface for EEG, ECoG, EOG, EMG, ECG, ... and physical data analyses and documentation under MATLAB
extensive tools for data processing in time, spatial and frequency domain
powerful 2-D and 3-D visualization tools to rapidly generate publication ready figures
enhancement of power with g.tec's specialized EEG, aEEG, ECG, CLASSIFY and High-Resolution EEG toolboxes
capability to integrate other MATLAB toolboxes as well as customers specific algorithms

can be used to analyze data from:

g.Recorder
Highspeed On-line Processing for Simulink
MATLAB and C API
and many other 3rd-party recording devices

g.BSanalyze: Base Version

The Base Version of g.BSanalyze allows the visualization, processing and basic analyses of EEG, ECoG, ECG, EOG, EMG, respiration, pulse, ... and physical signals. An intuitive data editor allows you scrolling through the data set, adding annotations and comments. Semi-automatic artifact detections and manual correction possibilities yield highest quality data for further investigations. Data set triggering on events and event-related signal changes can be performed based on markers, and signal channels. Temporal filtering and spatial filtering (e.g. Common Spatial Patterns, ICA, PCA) allow extracting hidden information from data sets.

g.BSanalyze base version brochure (PDF 0.9 MByte)

g.BSanalyze: Specialized Toolboxes

g.BSanalyze is completed by specialized toolboxes for EEG analyses, amplitude integrated EEG - aEEG, ECG analyses, Data set classification and a toolbox for High-Resolution EEG analyses.

The EEG - Toolbox includes specialized functions for EEG data analysis, parameter extraction, result presentation according to an editable electrode arrangement, source derivation calculation and various methods for spectral analyses and comparison. Cross-correlation methods and coherence analysis yield signal similarities in time and frequency domain.
The ECG - Toolbox is specialized for processing ECG data and to assess heart rate time course and heart-rate variability (HRV) patterns. Geometric measures, time domain statistical measures and frequency domain methods allow to determine the sympathetic and parasympathetic influence of the autonomeous nervus system.
The CLASSIFY - Toolbox enables to categorize patterns and signal features of biosignals into different classes. One application of classification is to discriminate EEG patterns in brain-computer-interface experiments (e.g. into LEFT and RIGHT hand motor imageries). Optimized feature selection can be performed via Distinction Sensitive Learning Vector Quantization (DSLVQ). This method yields the most relevant features for optimal data set classification.
The HReeg - Toolbox allows to combine EEG analysis with the anatomy of the brain (MRI/FMRI/CT data). Spatio-temporal analyses of multi channel EEG data yield new insights in brain function and allows to visualize spatial patterns and brain phenomena.
The CFM - Toolbox allows to calculate the amplitude integrated EEG (Cerebral Function Monitor - CFM signal) from any recorded EEG channel. Additionally the aEEG patterns are classified into specific patterns such as continuous voltage, discontinuous voltage, bursts, iso-electric patterns... The toolbox is of special interest for the neonatal intensive care unit (NICU).

g.BSanalyze, Base Version: Data Processing examples

Artifact Detection and Annotation

The g.BSanalyze data editor enables you scrolling comfortable through your data set. You have the choice between manual stepping or you take advantage of the data player which allows for automatic stepping at a user defined speed.

You can add/remove comments to specific data segments or mark channels or whole trials using different attributes (e.g. using marker ARTIFACT).

Hence you can selectively include or exclude channels/trials and time segments from further computations.

A data scoring facility enables you categorize different segments of your data, e.g. adding score REM sleep to EEG traces displaying rapid eye movement activity. Data scores can be loaded and saved for the specific data set.

Fourier Transformation and Band Power distribution

The data editor enables to easily investigate the power spectral density of selected signal segments during the review of your data set.

Simply Select the interesting time segment by using the Epoching tool and click on Analize.

The figure to the left displays the power spectral distribution for the selected EEG time segment. In this case a prominent rhythmic activity with high amplitude in the lower beta band can be seen.

The lower part of the figure displays the power contribution of the individual frequency bands. Hence alpha rhythmic activity, mu-rhymthmic activity or theta and delta activity can easily be verified.

A measure tool for measuring e.g. peak amplitude and peak frequency completes the tool.

Publication ready plots with the latest algorithms: Spatial Distribution of Independent Components

The method of Independent Component Analysis (ICA) separates statistically independent source signals that have been mixed linearely into distinct output signals. In contrast to Principal Component Analysis, ICA finds temporally independent components which may have also very similar scalp distributions.

One application of ICA is EOG artefact reduction and correction. The ICA output yields the time course of EOG source signal extracted from scalp EEG. The EOG signal can then be eliminated in a further processing step revealing the "cleaned" EEG for further processing and analyses.

Powerful Batch Processing and use of user-defined algorithms: Batch Processing and journal file

[...]

%Filter

Filter.Realization='fft';

Filter.Type='BP';

Filter.Order=0;

Filter.f_high=13;

Filter.f_low=7;

TrialExclude=[];

ChannelExclude=[3];

P_C=gBSfilter(P_C,Filter,ChannelExclude,TrialExclude);

[...]

Data set processing steps and necessary parameter settings are typically performed per mouse-click in the GUI. However, once the processing steps are fixed then group study data can be processed automatically in g.BSanalyze Batch mode. Furthermore all computation steps are well documented in a journal file allowing to follow the processing chain step-by-step.

Tutorials

Brain Computer Interface Manual (PDF 1.6 MByte) - The document describes the steps to classify the brain-computer interface EEG data with the bandpower algorithm and a linear discriminant analysis. Enjoy trying.

Package includes:

Software modules
help manual
hardlock

Technical requirements:

MATLAB
Signal Processing Toolbox

Additional info

ECG toolbox I

g.ECGtoolbox is a software package for ECG data processing and analysis. — The investigation of patterns and signal features of ECGs allows to observe noninvasively brain and heart functions and disfunctions. The graphical user interface for g.ECGtoolbox enables you to investigate all important time and frequency domain features of your electrocardiogram (ECG) data such as RR intervals or HRV maps.

Classify

g.CLASSIFYtoolbox is a software package for linear and non-linear data set classification. — Classification enables to categorize patterns and signal features of biosignals into different classes.

High-Res EEG

g.HReeg toolbox is an interactive environment for high resolution EEG data processing. — The investigation of patterns and signal features of EEG data combined with anatomical information based on MRI or CT data allow to observe noninvasively brain functions and disfunctions.

EEG toolbox

g.EEGtoolbox is an interactive environment for spatio-temporal EEG data processing. Specialized functions for EEG artifact detection and data quality computations assure highest-quality results. Adaptive Filtering or bandpower analyses allow to extract EEG signal parameters. The parameter extraction section includes also the extraction of Hjorth and Barlow parameters.

Cerebral function

The toolbox allows to calculate the amplitude integrated EEG (aEEG) signal from any already recorded EEG signal. The aEEG signal is also known as cerebral function monitor signal (CFM) and is used in the neonatal intensive care unit. The aEEG signal gives a time compressed activity report of the ongoing EEG signal and has gained a widespread popularity as an alternative to the conventional EEG monitoring in neonates.

ECG toolbox II

The ECG toolbox part II includes a single beat editor, automatic beat-by-beat detection of characteristic points: Pon, P, Poff, QRSon, Q, R, S, QRSoff, Ton, T, Toff, time evolution plots for parameters, QT-interval and ST-segment analysis for identification of pathological changes and classification of single beats.

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