Electroencephalography Data Processor

ramework for Running Signal Processing Methods

Petr Jeˇzek

and Roman Mouˇcek

New Technologies for the Information Society, University of West Bohemia, Univerzitni 8, 306 14, Pilsen, Czech Republic

Department of Computer Science and Engineering, University of West Bohemia,

Univerzitni 8, 306 14, Pilsen, Czech Republic

eywords:

EEG/ERP, Signal Processing, Data Processing, Plug-in, Wavelet Transform, Matching Pursuit, Fast Indepen-

dent Component Analysis, Finite Input Response, Fast Fourier Transform, Workﬂow, Software as Service.

Abstract:

This paper introduces difﬁculties related to running of signal processing methods. Although several systems

that implement signal processing methods exist, their sharing and remote calling is not satisfactorily solved.

Authors present a custom server-side approach that provides a powerful plug-in engine for integration of signal

processing methods. The plug-in engine ensures high modularity and ﬂexibility of the system. Since the

implemented methods are accessible via the SOAP Web Service, integration with another system is available.

There is also possible to use the system locally via a web browser. The set of basic methods is already

implemented and presented. The architecture and the most important parts of the system are also presented.

1 INTRODUCTION

In our research group we specialize on research of

brain activity. During our experiments we largely use

the methods of Electroencephalography (EEG) with

its subset Event-Related Potentials (ERP). Experi-

ments are performed in a neurophysiological labora-

tory including recording devices, a car simulator or a

soundproof booth. When experiments are performed

experimental data/metadata are collected for future

processing. Since neuroscience community is facing

problemswith the long-term storage of data/metadata,

raw data analysis, data/metadata sharing or design of

experimental protocols International Neuroinformat-

ics Coordinating Facility (INCF)

released recom-

mendations (Van Pelt and van Horn, 2007) for han-

dling of the neurophysiological data.

The Czech INCF National Node cooperates on the

deﬁnition of a standardized data/metadata format for

electrophysiology research. As an initial step we de-

veloped a custom solution called the EEG/ERP Portal

(Jezek and Moucek, 2010).

Data are stored within the EEG/ERP Portal and

they are accessible through a web browser. In ad-

dition, we provide data in the Semantic Web form

available for automatic readers. The Semantic Frame-

work (Jezek and Moucek, 2012) that we developed

ttp://www.incf.org/

transforms data from the EEG/ERP Portal into the Se-

mantic Web languages. The EEG/ERP Portal is reg-

istered within the Neuroscience Information Frame-

work (NIF) (Gardner et al., 2008); it ensures easier

searchability of stored experiments. The EEG/ERP

Portal is also integrated with other developed systems

(e. g. JERPA (Jezek and Moucek, 2011)) via Web

Service API.

Because data from stored experiments are usu-

ally further processed using various signal processing

methods, we are presenting the EEG Data Processor

that enables running of signal processing methods re-

motely.

2 STATE OF THE ART

When data are collected they are supposed to be fur-

ther processed. When third-party signal processing

methods are to be used, data need to be downloaded

and processed locally. The obtained results are not

stored as well.

When we were looking for a suitable tool, we

found that the set of available frameworks limited.

The following existing tools are able to process

EEG/ERP experimental data.

The Carmen Portal (Watson et al., 2007) provides

a set of implemented analytical tools. A user can up-

357

Ježek P. and Mou

cek R..

Electroencephalography Data Processor - Framework for Running Signal Processing Methods.

DOI: 10.5220/0004246303570361

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2013), pages 357-361

ISBN: 978-989-8565-37-2

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

load custom data into a local storage. Data can be ana-

lyzed and the results are putted into a virtual directory

assigned to users’ proﬁles. When the user wants to

add a custom analytical tool he/she can upload it into

the system. When this method is approvedby Carmen

administrators they integrate it into the system.

Carmen is only one well-known system that en-

ables calling analytical methods remotely together

with the possibility to store experimental results.

The next approaches work locally without any

possibilities to save or share experimental results.

Modular Toolkit for Data Processing (MDP) (Zito

et al., 2008) is a data processing framework writ-

ten in Python. MDP is a modular framework that

Python programmers can extend by additional mod-

ules. Common users can call implemented modules

locally.

BioSig (Schlogl and Brunner, 2008) is a free

open source software library of biomedical signal-

processing tools implemented as a library for Matlab,

Octave and C/C++. The last version of the tool called

biosic4c++ can be also used in the Python. This sys-

tem contains additional submodules e.g. for real-time

data processing or a signal viewer.

Since data processing often requires usage of

more methods sequentially, development of speciﬁc

workﬂows is required.

Taverna (Hull et al., 2006) is an open source and

domain-independent Workﬂow Management System

developed by

Grid

team. It is a suite of tools used

to design and execute scientiﬁc workﬂows and aid in

silico experimentation.

E-Science Central (Watson et al., 2010) combines

three emerging technologies Software as a Service,

Social Networking and Cloud Computing.

The Carmen developers are also developing CAR-

MEN Workﬂow Tool. It is a Java-based approach de-

signed to support both data and control ﬂow. It allows

parallel execution of services using a Service Invoca-

tion API.

3 EEG DATA PROCESSOR

3.1 Existing Approaches Difﬁculties

Although described solutions work quite satisfacto-

rily, several difﬁculties stay. The most of tested tools

work as stand-alone systems that a user has to in-

stall on a custom computer and execute manually.

The tools are implemented using various program-

ming languages where each requires a speciﬁc run-

http://www.mygrid.org.uk/

time environment. It could be an obstacle for many

users because it requires a certain level of user knowl-

edge. The last disadvantages are inability to inte-

grate such systems with users solutions and control-

ling them from a custom client program.

3.2 System Scope

Because of difﬁculties mentioned in Subsection 3.1

we have decided to implement a custom system that

enables running of signal processing methods on data

stored within the EEG/EEP Portal. The system is pre-

pared to be integrated with the systems of other users

or to be used independently.

The implemented methods are plug-ins based on

the Java platform. The libraries and the whole system

are provided as an open source. It ensures a higher

availability for potential users and faster development

of new plug-ins. Basically, the system is a lightweight

wrapper for implemented plug-ins with a double type

user interface: The ﬁrst is a standardized Web Service

API, the second one is an interactive web interface.

The focus of the EEG Data Processor is to be a

software as a service (SaaS) delivery model. It offers

reuse of software applications, ability to compare pro-

cessing algorithms and access to considerable com-

puting resource for high intensity computing tasks.

4 DESIGN AND REALIZATION

4.1 Overall Architecture

The system is a layered architecture. This architec-

tural style is supported by used programming lan-

guages and technologies (Java, Maven, Spring, Hi-

bernate, Apache CXF web services etc.)

The internal structure consists of several compo-

nents. Figure 1 shows the main components of the

system together with EEG base (Jezek and Moucek,

2010) integration:

• EEG Binary Loading - it loads data from binary

ﬁles obtained from an analogue-digital converter.

We currently support data obtained from the Brain

Vision Recorder but the system is fully prepared

for adding support of a new data format.

• Processing Resource Pool - Since performance

capacity of the hosted server is limited, we have

implemented a pool of available resources. The

system can be conﬁgured to manage a number

of requests simultaneously. When this limit is

reached, other requirements are queued and grad-

ually processed.

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Figure 1: Component Model.

• EEG Processing Algorithms - This module man-

ages 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 exe-

cution of a requested method. It parses the method

parameters and takes the method result.

4.2 User Interaction

4.2.1 Web Service Connector

Since the EEG Data Processor is supposed to be ac-

cessed by external clients’ systems, the Web Service

API best matches this requirement. The system pro-

vides a simple interface with several basic methods.

The client can obtain a list of installed methods, lists

of required parameters for the speciﬁc method, get

available processing units and call the method that

processes input data. Such interface provides a suf-

ﬁcient list of operations in order to be integrated with

any client system. A usage of SOAP web services

ensures easy integration by a well-deﬁned protocol.

4.2.2 Web Interface

Communication with a client system is ensured us-

ing Web Service Connector. If the user has only data

but not a custom system connected to the Internet,

a simple web based interface is prepared. Figure 2

shows the interface overview. The user can list in-

stalled signal processing methods and available pro-

cessing units. When he/she wants to upload custom

data, a simple upload form is prepared. The uploaded

data are stored within the user’s proﬁle and processed

on the background. When the result is available, a

link to download appears.

4.3 Customization Part

4.3.1 Plug-in Engine

A Plug-in engine is a central unit of the system. It

contain an implementation of a Processor. The Pro-

cessor is an abstraction of implemented EEG Binary

Loading libraries that are intended to read input digi-

talized signal stored using various data formats. The

Processor creates individual Processes. The Process

is an encapsulation of a speciﬁc method together with

the speciﬁc parameters needed to start the method.

4.3.2 Plug-in Requirements

When a new method is developing it has to satisfy

several conditions in order to be integrated into the

system. First it must contain one input method with

speciﬁed input parameters of the method and a XML

description of the output. Because of output of indi-

vidual methods can be different (e.g waveforms, dis-

crete points, etc.) the XML format ensures its easier

future representation. Second, the method has to have

a descriptive properties ﬁle that contains the name

of the input method, package and class. When the

method is invoked by the described External Method

Invoker, it reads all parametersfrom the properties ﬁle

and conﬁgures the created Process.

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Figure 2: Web Interface Overview.

4.4 System Security

Since the system is public available through the Inter-

net, secured access has to be ensured. The system is

protected by system accounts with two possible user

roles. The user can upload custom data that can be

processed while the administrator can manage regis-

tered users and conﬁgure the system. At the imple-

mentation level, the security is ensured by Spring Se-

curity framework with protected user passwords by a

cryptographic hash function with randomly generated

salt.

When the user accesses the system through the

Web Services API, the SSL transfer is ensured and

users credentials are required.

5 IMPLEMENTED PLUG-INS

We have already implemented methods that are most

often used during experiments within our laboratory.

Implemented methods are common Java libraries ex-

tended according to the description in Section 4.3.1.

It ensures their implementation outside the system in-

dependently. We provide implemented plug-ins open

source hosted on the public repositories (GitHub, Sor-

ceForge). The list of implemented methods is gradu-

ally expanding.

Currently, we have implemented the following

methods:

• Complete and Discrete Wavelet Transform (Mal-

lat, 1999) are a class of a functions used to localize

a given function in both space and scaling; and has

some desirable properties compared to the Fourier

transform. The transform is based on a wavelet

matrix, which can be computed more quickly than

the analogous Fourier matrix.

• Matching Pursuit (Ferrando et al., 2002) decom-

poses any signal into a linear expansion of wave-

forms that are selected from a redundant dictio-

nary of functions. These waveforms are chosen

in order to best match the signal structure. The

dictionary is often based on Gabor functions.

• Fast Independent Component Analysis

(Hyv¨arinen et al., 2001) is used for ﬁnding

a linear representation of nongaussian data so that

the components are statistically independent, or

as independent as possible. Such a representation

seems to capture the essential structure of data in

many applications, including feature extraction

and signal separation.

• Fast Fourier Transform (Rao et al., 2010) is an ef-

ﬁcient algorithm to compute the discrete Fourier

transform (DFT) and its inverse. the discrete

Fourier transform (DFT) is a speciﬁc kind of dis-

crete transform, used in Fourier analysis. It trans-

forms one function into another, which is called

the frequency domain representation.

• Finite Input Response is a digital ﬁlter that have

an impulse response which reaches zero in a ﬁ-

nite number of steps. It can be implemented non-

recursively by convolving its impulse response.

6 CURRENT STATUS

The system is almost fully implemented and prepared

for testing. We put it on the testing server where it

is tested by the research group members. After that

we provide the system for testing by the limited set

of selected collaborative partners. When testing ends,

we release the system on the production server.

7 FUTURE WORK

When the system is released, the next step will be an

implementation of a client side within the EEG/ERP

Portal. It allows computational portal users to work

more comfortably because the load will be distributed

between two servers. A faster response will be en-

sured.

Since a need to associate methods into workﬂows

was mentioned in Section 2, we plan to implement a

wokﬂow module into the system. This module will

ensure a development of custom workﬂows and the

possibility to store them within users’ accounts. An

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interface with a drag and draw functionality ensures

its easy usability.

When signal processing methods implemented in

various programming languages (e. g. Python,

C/C++, Pearl) exist, the next technological challenge

leads in providing a wrapper for a set of most often

used programming languages in signal processing.

Further, we plan to investigate possibilities in the

area of Cloud Computing, because of a potential load

of the server will increase together with the raising

number of users. The suitable cloud should help us to

improve management of system resources.

8 CONCLUSIONS

The difﬁculties related to the processing of data from

EEG/ERP experiments are presented. Since imple-

mentation of present signal processing methods is

usually intended for local usage we decided to pro-

pose and implement a custom system. The aim of the

presented system is to serve a wide community of re-

searchers to share experimental methods.

The system combines research in the EEG/ERP

domain with modern software engineering ap-

proaches. It helps to enhance research efﬁciency and

enables faster achievement of scientiﬁc results.

The most often used methods in our laboratory

are brieﬂy presented. The presented methods are al-

ready implemented and integrated within the system

as plug-ins. Due to a powerful plug-in engine in-

terested users are welcome to implement a custom

method according to a described procedure. We are

able to assume these plug-ins and incorporate them

into the system.

ACKNOWLEDGEMENTS

This work was supported by the European Regional

Development Fund (ERDF), Project ”NTIS - New

Technologies for Information Society”, European

Centre of Excellence, CZ.1.05/1.1.00/02.0090.

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