 Multi-modal off-line biosignal analysis under MATLAB
(PREREQUISITE: MATLAB 6.5, Signal Processing Toolbox
!) 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 non-invasively brain,
heart- and muscle functions and dysfunctions. |
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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 a unique package for biosignal analyses.
Highlights
- interactive and intuitive graphical user
interface for EEG, 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, 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.BSanalyze: Base Version
The Base Version of g.BSanalyze
allows the visualization, processing and basic analyses of EEG, 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: Specialized
Toolboxes
g.BSanalyze is completed by specialized
toolboxes for EEG analyses, ECG analyses, data set
classification and a toolbox for high-resolution EEG analyses.
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 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 Analze.
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
artifact reduction and correction. ICA output yields the time
course of EOG source signal extracted from scalp EEG. The EOG signal can
then by 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
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 MB, 22 pages) - The document
describes the steps to classify the brain-computer interface EEG data with
the bandpower algorithm and a linear discriminant analysis. This tutorial
can be performed with the g.BSanalyze demo version. The EEG data comes also
with the demo version. Enjoy trying.
Package includes
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Software modules
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help manual
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hardlock
Technical Requirements
MATLAB, Signal Processing Toolbox
Copyright © g.tec
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