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EEG/ERP Analytic Tools Management System
Jan Štěbeták, Tomáš Řondík, Roman Mouček, Department of Computer Science and
Engineering, Faculty of Applied Science, University of West Bohemia, Pilsen, Czech
Republic.
Correspondence:
Jan Štěbeták
University of West Bohemia
Department of Computer Science and Engineering
Univerzitní 22,
Pilsen, 30614, Czech Republic
stebjan@kiv.zcu.cz
Abstract:
Neuroinfomatics laboratories produce a lot of experimental data, which has to be stored and
further processed. This paper introduces a management system for signal processing and
sharing analytic tools. Although several systems for signal processing exist, their sharing is
not satisfactorily solved. Authors present a server-side approach, which provides processing
signal via analytic tools. Since the implemented methods are accessible via the SOAP Web
Service, integration with system providing data storage is implemented. Integration and
sharing signal processing methods with other systems is available. The set of methods is
implemented and presented. The design of workflows is also presented.
Keywords: Electroencephalography; Event-related potential; Analytic tools, Web Services;
Integration; Workflows
1 Introduction
In our research group we specialize of research of a brain activity. During our experiments we
widely use the methods of Electroencephalography (EEG) with its subset Event-Related
Potentials (ERP). Experiments are performed in a neuropsychological laboratory including
recording devices or a car simulator. When experiments are performed experimental data and
metadata are collected for future processing. Since neuroscience community is facing
problems with the long-term storage of data and metadata, raw data analysis, or sharing data
and analytic methods, International Neuroinformatics Coordinating Facility (INCF, 2012)
released recommendations (Van Pelt and van Horn, 2007) for handling neurophysiologic data.
As members of the Czech National Node of International Neuroinformatics Coordinating
Facility (INCF) we cooperated one the definition of data and metadata format for
electrophysiology research. Our efforts resulted in a custom solution – the EEG/ERP Portal
(Jezek and Moucek, 2010). Data are stored within the EEG/ERP Portal and are accessible via
a web browser.
Because data from stored experiments are usually further processed using various signal
processing methods, the EEG Data Processor (Jezek and Moucek, 2013) is presented.
This paper describes methods for obtaining ERP components from EEG records and analytic
tools for detection ERPs. Since scientists appreciate the possibility to have experimental data
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and processing methods on the same place, we presented integration of analytic tools to the
EEG/ERP Portal. The last part of this paper deals with a design of workflows.
2 Materials and methods
This section briefly describes the development of neuroinformatics infrastructures, methods of
detection ERP components, development of methods for EEG/ERP signal analysis, and
system integration.
2.1 Neuroinformatics infrastructures
The neuroinformatics infrastructure is being built in several INCF national nodes in parallel.
The INCF portal includes e.g. a software center for easy storage and sharing of
neuroinformatics software tools, a content management system for national nodes presentation
and provides access to supercomputing resources for the neuroinformatics community.
Modular Toolkit for Data Processing (MDP) (Zito et al., 2008) is a data processing framework
written in Python. MDP is a modular framework that Python programmers can extend by
additional modules. Common users can call implemented modules locally.
The British CARMEN system (Watson et al., 2007) has been designed to allow neuroscientists
to share data and programs (services) from neurophysiological experiments. The ‘Portal’ is a
web interface onto the CARMEN system, and provides end‐users with access to the computer
and data storage resources which are at the core of the system.
The portal provides following set of features
 Search across archived data set
 Upload, annotate, and store experimental data
 Run processes and routines on the stored data on the CARMEN computers
The analysis services are implemented as ‘web‐services’ on the CARMEN system.
2.2 ERP components and methods
P300 component (Fig. 1) is a component obtained from an EEG signal which correlates with
the specific brain activity. This component appears as a brain response to an unexpected
external stimulus. The normal delay of the occurrence is approximately 300 ms after the
appearance of the stimulus.
Fig. 1
Example of P300 component
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The methods (Luck 2005) for analyzing a pure EEG signal are used especially in medicine for
detecting epileptic waveforms and sleep disorders, or for indicating brain death. The cluster
analysis and adaptable neural network are widely used. The fundamental method for obtaining
ERP components from the EEG signal is averaging.
A stimulus is repeated during an experiment and brain responses are averaged into one
response. Many subjects can be measured using one experimental scenario and these subjects
can be associated into subject groups depending on their age, gender, or disease. The grand
average is one representative brain response to an external stimulus in the whole group
2.3
Levels of System Integration
2.3.1 Data Layer Integration
Data integration involves combining data residing in different data repositories and providing
business users with a unified view of this data. The top batch-oriented technique that
companies utilize is known as ETL (Extract – Transform – Loading, Fig. 2) (ETL, 2011).
Fig. 2
Principle of ETL
ETL enables physical movement of data from source to target data repository. The first step,
extraction, is to collect or grab data from its source(s). The second step, transformation, is to
convert, reformat, cleanse data into format that can be used be the target database. Finally the
last step, loading, is import the transformed data into a target database, data warehouse, or a
data mart.
ETL tool providers:
Commercial ETL Tools:
 IBM Infosphere DataStage
 Informatica PowerCenter
 SAP Business Objects Data Integrator (BODI)
 SAP Business Objects Data Services
 Oracle Warehouse Builder (OWB)
 Oracle Data Integrator (ODI)
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
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Data Integration Studio
Microsoft SQL Server Integration Services (SSIS)
Ab Initio
SyncSort DMExpress
iWay DataMigrator
Pervasive Data Integrator
Freeware, Open Source ETL tools:
 Pentaho Data Integration (Kettle)
 Talend Integrator Suite
 CloverETL
 Jasper ETL
2.3.2 Application Layer Integration
Remote Procedure Call (RPC) is widely used for constructing distributed, client-server based
applications. A client application calls a remote procedure (method), transfers data to a server
application, and waits for a result. For web applications there is used Web Services
technology (Jie Liu et al., 2006).
Web Services uses XML messages and HTTP protocol (HyperText Transfer Protocol). Web
Services use XML Schema for data type definition and XML Namespaces for objects
identification. Web Services includes three cores:
 SOAP (Simple Object Access Protocol)
 WSDL (Web Services Definition Language)
 UDDI (Universal Description Discovery and Integration)
SOAP is a simple XML-based protocol that allows to communicate applications information
over HTTP without the dependency of OS platform. SOAP uses HTTP and XML as the
mechanisms for information exchange.
Current free and commercial implementations available for Web Services:
 Apache SOAP, Axis 1 and Axis 2. SOAP and Axis 1 are now obsolete; use Axis 2
instead.
 JAX-WS Reference Implementation
 JAX-RS Reference Implementation
 Metro (includes the JAX-WS reference implementation)
 Apache CXF (formerly called XFire)
 MS.NET
 Java 6 includes the JAX-WS reference implementation (and a minimal server for it)
Cloud computing is the use of computing resources (hardware and software) that are delivered
as a service over a network (typically the Internet). The name comes from the use of a cloudshaped symbol as an abstraction for the complex infrastructure it contains in system diagrams.
Cloud computing entrusts remote services with a user's data, software and computation.
2.3.3 Presentation Layer Integration
Web portals (Ingyin, 2006) are a means for presentation level integration of enterprise
application and services. Web portals allow the users to select the links they would like on
their personal page. A portal is built upon layers of services and component modules. Portlets
are used by portals as pluggable user interface components that provide a presentation layer to
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information systems. Portal provides Web based personalizable and integrated system to
access internal and external application which support user process and implement front-end
integration.
3 EEG/ERP Portal
Our research group has developed an EEG/ERP Portal (fig. 3) because of a need for storage of
EEG/ERP experiments gained from encephalography research. This portal enables research
groups to store, manage and download their experimental data and metadata. Portal is
developed as a standalone product running on servers in our department. The usage of the
Portal does not require any special software installation, only a web browser.
The portal application is based on Spring MVC (Spring, 2011) technology (Model – View –
Controller). Access to the EEG database is ensured by framework Hibernate (Hibernate,
2011). This framework is used for object-relational mapping. It also protects our portal
against SQL injection attack among other things. The portal uses JSP technology (Java Server
Pages) including JSTL for displaying HTML pages into user´s browser. The Portal provides:
 Management of EEG/ERP data and metadata
 Management of EEG/ERP experimental design (experimental scenarios)
 Management of data related to tested subjects
 Sharing of knowledge and working within groups
 Content management system
 Full-text and advanced search
The data are protected by the system of user accounts with defined user roles. Individual users
are grouped into self-managed groups. On the basis of activities that the user can perform four
user roles are recognized (Reader, Experimenter, Group Administrator, and Supervisor). The
user who wants to upload or download experiments has to create an account and to become a
member of at least one group.
Fig. 3
EEG/ERP Portal frontend
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4
Analytic Tools
This section describes methods that we use for obtaining ERP components, removing
artifacts, and detecting the P3 component. Averaging is the fundamental method for obtaining
ERP components and removing artifacts. We use the Fast Fourier Transform and Fast ICA
algorithm for detecting and removing artifacts. For detecting the P3 component there are used
following set of methods: Matching Pursuit, Discrete and Continuous Wavelet Transform, and
Hilbert-Huang Transform.
Fourier transform (FT) is the oldest method of signal analysis. This method allows you to
convert the signal from time domain to the frequency, which means that it can detect all
frequency components occurring in the signal, and level of their representation [9]. Its discrete
variant is often used in computer science. We use FFT for detecting frequency in EEG/ERP
signal during experiments with steady-state potentials. FFT is also used in implementation of
matching pursuit algorithm according to [820]. We implemented two methods for signal
spectrum estimation as well – BlackmanTukey.
The matching pursuit (MP) (Vareka, 2012) algorithm decomposes any signal to the sum of socalled atoms, which are selected from the dictionary. The atom that best approximates the
input signal is chosen in each iteration. This atom is subtracted from the input signal and the
residue enters the next iteration of the algorithm. The total sum of atoms selected successively
in algorithm iterations is an approximation of the original signal - more iteration we do, more
accurate approximation we get. The algorithm was originally described in [MP]. We decided
to implement this method according to [820] where the Gabor dictionary is used.
For displaying results we implemented the time-frequency transformation known as WignerVille transformation. The input of this transformation is the set of chosen atoms. Energy of
atoms shows the occurrence of P3 waveform.
Wavelet Transform (WT) (Ciniburk et al., 2010) is a suitable method for analyzing and
processing non-stationary signals such as EEG. For EEG signal processing it is possible to
use continuous wavelet transform (CWT) or discrete wavelet transform (DWT). Both CWT
and DWT were tested during our research focused on automatic ERP detection. WT is a
suitable method for ERP detection because it has a good time and frequency localization.
DWT is common in computer science because of high performance caused by its algorithmic
complexity. We’ve implemented a lot of wavelets which can be used in DWT. The reason is
that for our research in automatic ERPs detection is necessary to have a wavelet which
corresponds to detected ERP as much as possible. And there are many ERPs with different
waveforms.
Available wavelets: Coiflet6, Coiflet12, Coiflet18, Coiflet24, Coiflet30, Daubechies4,
Daubechies4, Daubechies6, Daubechies8, Daubechies10, Daubechies12, Daubechies14,
Daubechies16, Daubechies18, Daubechies20, Haar, Symmlet4, Symmlet6, Symmlet8.
CWT is often replaced in computer science by its discrete form because of its algorithmic
complexity. However, we decided to make this method accessible for signal processing in
EEG Portal. The reason is size of step by shifting a wavelet on original signal and is described
in [Souhyho diplomka] in detail.
Available wavelets: ComplexGaussian, ComplexMorlet, Gaussian, MexicanHat, Morlet.
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Independent Component Analysis (ICA) (Hyvärinen et al., 2001) is a well-known method for
blind signal separation and signal deconvolution. In the EEG/ERP domain, ICA can be used
for artifact removal, ERPs detection, and – generally speaking – for detection and separation
of every signal which is independent on EEG activity.
We implemented four common filter types: low pass, high pass, band pass, and band reject,
which can be parameterized to get a desirable filter. These filters allow us to get rid of
undesirable frequencies from input signal. This is typically first step in EEG/ERP processing
because we know which frequencies are common in this kind of signal – any other frequency
is regarded as noise.
5
Results
5.1 EEG Data Processor
EEG Data Processor is a web based application, which offers analytic tools for EEG/ERP
signals. This application does not need any installation, only web browser. The frontend
design is on figure 4.
Fig. 4
EEG Data Processor frontend
This application is written in Java language. We use the Spring Framework and AJAX
technology for processing HTTP requests. For connection to the database we use the
Hibernate framework. To the database there are stored registered users, which are permission
to use the EEG Data Processor. The authentication process is ensured by Spring Security. The
application design is on figure 5.
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Fig. 5
EEG Data Processor architecture
The internal structure consist of several components:
 EEG Binary Loading – It loads data from binary files obtained from an analoguedigital converter. We currently suppord data obtained from the Brain Vision Recorder.
 Processong Resource Pool – Since performance capacity of the hosted server is
limited, we have implemented a pool of available resources. The systém can be
configured to manage a number of requests simultaneously.
 EEG Processing Algorithms – This module manages a running of installed plug-ins. It
has access to the list of installed plug-ins and calls a method invoker.
 External Method Invoker – Is responsible for execution of a requested method. It
parses the method parameters an takes the method result.
5.1.1 Supported Data Format
Since there are not standardized format for EEG/ERP domain, we use data format, which is
provided by Brain Vision Recorder. This format includes binary data file .eeg where signal
values are encoded. This data format encoding is described in a header file .vhdr. An example
of a header file is given below.
[Common Infos]
Codepage=UTF-8
DataFile=rab.eeg
MarkerFile=rab.vmrk
DataFormat=BINARY
MULTIPLEXED=ch1,pt1,ch2,pt1…
DataOrientation=MULTIPLEXED
NumberOfChannels=21
; Sampling interval in microseconds
SamplingInterval=1000
[Binary Infos]
BinaryFormat=INT_16
[Channel Infos]
Ch1=Fp1,,0.1,μV
Ch2=Fp2,,0.1,μV
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Ch3=F3,,0.1,µV
Ch4=F4,,0.1,µV
According to the header file, we have implemented the reader of the binary file. The output
of this reader is a field of double numbers (signal values).
The last type of file produced by Brain Vision Recorder is a marker file .vmrk. This file
contains positions of stimuli in the EEG signal. An example of a marker file is given below.
[Common Infos]
Codepage=UTF-8
DataFile=rab.eeg
[Marker Infos]
Mk1=New Segment,,1,1,0,20100223094409773314
Mk2=Stimulus,S 1,32661,1,0
Mk3=Stimulus,S 2,34481,1,0
Mk4=Stimulus,S 3,36301,1,0
Mk5=Stimulus,S 4,38121,1,0
Mk6=Stimulus,S 5,39941,1,0
The EEG Data Processor is fully prepared for adding support of a new data format.
5.1.2 Adding New Methods
Currently, we design a plug-in system for registering and adding new analytic tools. This
system allows adding new methods automatically. Nevertheless, we are focused only for
methods developed in Java language. These JAR libraries have to follow the criteria:
 The initialization method must be void type
 XML description of the output. Because output of individual methods can be different,
the XML format ensures its easier future representation
 To the JAR libraries must be attached a setting file, where the initialization Method
have to be described
An example of the setting file is given bellow.
algorithmName=Matching pursuit
author=Anonymous
main.class=pilsner.matchingPursuit.present.RunnerMatchingPursuit
main.method=runMatchingPursuit
The next challenge leads in providing a wrapper for a set of most often used programming
languages in signal processing such as Python, C/C++, Pearl, or Matlab.
5.2
Integration of Applications
5.2.1 Purpose of Integration
The purpose of integration is to enable users of EEG/ERP Portal analyzing their data using
analytic methods mentioned in Chapter 4. The second goal is to allow sharing analytic tools
using this application as a third party service provider.
Since analytic methods are time-consuming and computationally demanding, EEG Data
Processor is running on another server than EEG/ERP Portal. This separation ensures that
users of EEG/ERP Portal are not influent by running analytic methods.
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Because of sharing methods, the data layer integration has been rejected. This approach only
allows sharing data between both applications. However, data stored in EEG/ERP Portal
database would be available for EEG Data Processor; users of EEG/ERP Portal would have to
open EEG Data Processor in their browser and then analyze their data. We decided for the
business layer integration, which is based on remote procedure call. This approach allows
using analytic tools via EEG/ERP Portal. It also provides sharing these tools, because remote
methods can be called from many applications.
5.2.2 Implementation
The technology used for remote procedure call is known as Web Services technology. This
technology is suitable for point to point integration for web based applications. Currently, we
have two web based applications (EEG/ERP Portal and EEG Data Processor). Both
applications are based on Spring framework. Apache CXF is frequently used implementation
of Web Services technology for such applications.
Apache CXF is an open source services framework that makes web service development easy,
simplified, and standard based. This technology is used for client-server based integration of
web applications. The client application will prepare an order and send it to the server
application through a business method call. The server application will contain a web service
that will process the order. For implementation we use code-first approach. It includes three
following steps:
 Create a Service Endpoint Interface (SEI) and define a business method to be used
with the web service.
 Create the implementation class and annotate it as a web service.
 Create an xml configuration of the service class and a Spring bean using JAX-WS
frontend.
The interface below is the Service Endpoint Interface defying methods, which EEG Data
Processor provides.
@WebService
@Secured("ROLE_USER")
public interface ProcessService{
/**
* Returns number of currently available processing units.
* @return available processing units
*/
public int availableProcessingUnits();
/**
* Getter of parameters necessary for method to run.
* @param fileFormat supported file format
* @param methodName name of desired process method
* @return array of parameters
*/
public MethodParameters[] getMethodParameters(SupportedFormat fileFormat,
String methodName);
/**
* Returns byte array of processed data
* (will be replaced by output format in time)
* @param data files to be processed
* @param fileFormat one of supported file formats
* @param algorithmName name of processing algorithm
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* @param params other parameters
* @return bytes of processed data
*/
public byte[] processData(DataFile[] data, SupportedFormat fileFormat,
String algorithmName, String[] params);
/**
* Getter of available algorithm names.
* @return algorithm names
*/
public String[] getAvailableMethods();
}
The interface and the implementation have been created. It is necessary to configure an
endpoint in an xml file. In EEG Data Processor the webservice.xml file is used. Configuration
is given below.
<bean id="processService"
class="cz.zcu.kiv.eegprocessor.webservice.ProcessServiceImpl"/>
<jaxws:endpoint
id="personService"
implementor="#processService"
address="/webservice/processService">
<jaxws:properties>
<entry key="mtom-enabled" value="true"/>
</jaxws:properties>
</jaxws:endpoint>
</beans>
Id specifies a unique identifier for a bean, implementor specifies the actual web service
implementation class, and address specifies the URL address where the endpoint is published.
The URL address must be relative to the web context.
5.3 Design of Workflows
Since data processing often requires usage of more methods sequentially, development of
specific workflows is required. Apart from sharing the methods described above, we will
provide an opportunity to create, use, and share workflows in the EEG/ERP Portal.
Workflows will simplify the work with data and methods and offer more comfort to users.
Now we are working on design and technological aspects of workflows. We need to extend
the database by the tables for workflows defined by users. There are two types of analytic
methods:
 Signal processing methods such as Matching Pursuit or Wavelet Transformation
 Signal preprocessing methods such as averaging or filtering
Technically, workflow means to put analytic methods into a pipe, where an output from the
previous method becomes an input to the next method. The signal processing methods modify
the signal; their input and output have the same format. These methods are compatible and
could be put anywhere into the pipe. Signal processing methods provide result in other
format.
We consider several approaches, how to deal with the incompatibility of provided output
formats. The first idea consisted in putting signal processing methods at the end of the pipe
because of the other output format. This approach would allow modifying an EEG/ERP signal
using signal preprocessing methods and analyze it as one task. Nevertheless, this approach
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would not allow further processing of analytic result. There are also methods for visualization
of this result. These methods should be put into the pipe after signal processing methods.
Considering this point, we are developing a workflow management system. This system will
check the compatibility of input and output parameters. An example of parameters type and
explaining of the compatibility is given below.
 Output parameter type from averaging method is an array of double values (EEG/ERP
signal). Input parameter type of Wavelet Transformation is the array of double values
as well. These parameters are compatible.
 Output parameter type from Matching Pursuit method is a two-dimensional array
(values of atoms). Input parameter type of scalogram, which is used for visualization
of result is a one-dimensional array. Parameters of these methods are not compatible.
Scalogram is used for visualization of Wavelet Transformation result.
This management system will solve the issue about further processing of analytic result.
However, parameters, which are syntactically compatible, do not have to be semantically
compatible as well. It means, one-dimensional arrays match syntactically but in case of
averaging, output is an EEG/ERP signal. Output format of Wavelet Transformation is a onedimensional array as well, but in this case it returns computed coefficients.
As a solution of this issue we will extend our database. It will provide a list of available
methods, input and output parameters type, and description of these parameters. This
information will be provided to users, who will create their own workflows. A user will
decide which method he/she put into the pipe and he/she also decides the order of these
methods. The graphic user interface will be developed, which allows creating and saving
user´s own workflows.
Workflows are organized as a tree structure (Fig. 6), where each branch of the tree has the
same meaning as a pipe in Linux; an output of the method serves as an input of the next
method.
Fig. 6
Example of a workflow tree
5.4 Tests
We did some performance tests to be sure that our encapsulation of discrete signal processing
methods and their integration into EEG portal doesn’t have a negative impact on its
performance.
5.4.1 Methodology
We run four test routines for each discrete signal processing method. Each routine with
different number of samples in input signal: 256, 512, 1024, and 2048 (we used real
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EEG/ERP signal, but there is no difference in performance which depends on input signal
characteristic). Each routine was repeated one thousand times to minimize measuring error
caused by third parties (CPU task planning, asynchronous events, etc.). Our effort was keep
the same conditions for all methods as much as possible – we used the same configuration
(Win 7 Professional SP1 x86 with Java Runtime Environment 7 on Intel Core 2 Duo 2 GHz,
1066 MHz FSB, 3 MB L2 cache, 3 GB DDR2 RAM – no multithreading was used) and only
one application - which was started by user - was our test routine started from terminal.
5.4.2





Setup
DWT: Haar wavelet, all possible wavelet strengths were computed
CWT: Mexican hat wavelet, step 1, wavelet strength set to 60
MP: Gabor base was used, decomposition to 10 atoms
FastICA: decomposition to 10 independent signals
FFT: there is no parameter to be set up
5.4.3 Test result
We obtain following results (Table 1):
Table 1. Test results
Average time in milliseconds for each discrete signal processing method
DWT
CWT
MP
FastICA
FFT
Number of
samples
256
512
1024
2048
542
543
544
0.044
0.058
0.078
0.153
10.002
55.685
230.468
709.730
0.225
0.552
0.621
1.317
0.136
0.219
0.410
0.797
Results are also displayed in graphs (Fig. 7):
DWT, FastICA, FFT
800
1.4
700
1.2
Time in milliseconds
Time in milliseconds
CWT
600
500
400
300
200
100
0
1
0.8
DWT
0.6
FastICA
0.4
FFT
0.2
0
256 512 1024 2048
Number of samples
545
1225.988
4941.827
21320.228
89471.612
256
512
1024
Number of samples
2048
MP
Time in seconds
100
80
60
40
20
0
256
512
1024
2048
Number of samples
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
Fig. 7
Test of signal processing methods
The results look like we expected. Performance of DWT, FastICA, and FFT is in normal and
we expect that there is no reason to do some performance optimization in future. CWT is
implemented as brute-force algorithm so its performance is not a surprise. The performance of
matching pursuit algorithm is worse.
6 Discussion and conclusion
Although several systems that implement signal processing methods exist, their sharing and
remote procedure calling is not satisfactorily solved. We have developed a neuroinformatics
infrastructure. This infrastructure is composed of two web based application. Since
neuroinformatics research produce lot of data, the EEG/ERP Portal was developed. The
EEG/ERP Portal enables research groups to store, manage, and download their experimental
data and metadata. The second application, EEG Data Processor enables to analyze
experimental data.
Remote procedure call is widely used for business layer integration of application. For web
based applications there is used Web service technology. Since both applications are
developed using Spring framework, we use Apache CXF implementation of Web services.
This integration allows users of EEG/ERP Portal analyzing their data using methods
implemented in EEG Data Processor. Since sharing data and tools is very helpful for scientific
community, used technology also provides analytic methods of EEG Data Processor to a
third-party application. But using of this technology for sharing analytic tools has one
disadvantage. A developer of a third-party application, who wants to integrate analytic tools
provided by EEG Data Processor, is forced to modify his/her application. It is necessary to
implement the Web services client into this application.
Since data processing often requires signal preprocessing before using signal processing
method, the purpose of workflows is presented. According to this design we need to extend
our database and then we will develop a workflows management system and graphic user
interface for creating and running workflows via web browser.
We plan to investigate possibilities in the area of Cloud Computing. The suitable cloud should
help us to improve management of system resources. We also plan to investigate and develop
plug-in system, which allows users to add new analytic methods written in various
programming language, not only in Java.
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Acknowledgment
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