Cloud Infrastructure for Storing and Processing EEG and ERP

Experimental Data

Petr Je

zek and Luk

s Va

reka

Department of Computer Science and Engineering, New Technologies for the Information Society,

Faculty of Applied Sciences, University of West Bohemia, Plze

n, Czech Republic

Keywords:

EEG, ERP, Cloud, HDFS, Hadoop, Spark, Experiment, Infrastructure.

Abstract:

Current infrastructures for experimental data, results and computational tools make a shift from locally main-

tained solutions to remote cloud-based infrastructures. It brings a higher availability, sustainability and per-

formance. However, speciﬁcs of different research areas require development of customized solutions for

individual research domains. For example, electroencephalography and event-related potentials (EEG/ERP)

use speciﬁc devices, data formats and machine learning workﬂows. As a solution, a cloud-based system for the

EEG/ERP domain containing a distributed data storage, a signal processing method library and a client GUI

is presented. The signal processing method library is used for training of classiﬁers and classifying the data

in the cloud-based system controlled by the GUI. The presented system was tested using a machine learning

workﬂow based on the data stored in the system. In the workﬂow, various classiﬁers were trained and their

parameters stored into the system. Finally, testing data were classiﬁed using previously trained classiﬁers.

1 INTRODUCTION

Laboratories have been using locally maintained

databases and tools for experimental data storage and

processing. Experimental results have been pub-

lished and then locally stored or often even supposed

as no more needed and discarded. Nowadays be-

cause of increasing data storage capacity, fast Internet

connectivity, and cheaper hardware equipment, the

amount of experimental data is dramatically increas-

ing. With increasing amount of data, requirements

to data sustainability (Van Horn and van Pelt, 2008),

experiments reproducibility, and publishing also orig-

inal data not only experimental results (Abelard and

eloise, 2011) are increasing, too. These require-

ments make locally maintained infrastructures insuf-

ﬁcient and obsolete. On one hand the computa-

tional performance is cheaper and easily available but

on the other hand the increasing number of devices

and technologies for data storing and processing re-

quire a higher demand on technical knowledge of re-

searchers.

A new view on data storage and management

makes a shift from locally maintained infrastruc-

tures to remote solutions usually referred to as cloud

computing. This approach deﬁnes a new deliv-

ery model Architecture-as-a-Service (AaaS) that sup-

poses a provider of a remote computational infrastruc-

ture who guarantees data integrity, backup and unin-

terrupted availability. Methods for data processing are

also installed in the remote infrastructure which en-

sures enough performance by scaling tasks over a dis-

tributed network of a computational cluster. This ap-

proach takes off the responsibilities from researchers

who can be focused on the laboratory work instead of

maintaining complex infrastructures.

Especially the domain of electroencephalography

(EEG) and event-related potentials (ERP) requires

various experts from signal processing, database or

hardware domains. Moreover, laboratory staff re-

sponsible for collecting experiments must be familiar

with all the systems in a complex chain of data col-

lecting, storing, processing and ﬁnally, presenting re-

sults. In the cloud computing approach the responsi-

bilities can be either partly dedicated to a paid service

or split out among individual experts not necessarily

located in the same laboratory.

We present a complex cloud-based infrastructure

for storing and processing experimental data. This in-

frastructure is validated on an ERP focused use-case

study. This infrastructure contains a data storage, a

library of signal processing methods, and a simple

GUI allowing users to easily control the whole sys-

tem. Section 2 presents similar concepts and eval-

274

Ježek, P. and Va

reka, L.

Cloud Infrastructure for Storing and Processing EEG and ERP Experimental Data.

DOI: 10.5220/0007746502740281

In Proceedings of the 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2019), pages 274-281

ISBN: 978-989-758-368-1

uates the approach. Section 3 brieﬂy introduces the

ERP domain and introduces a technological stack for

the system. Section 4 describes implementation of the

system. Finally, Section 5 presents testing of the sys-

tem and Section 6 concludes the work.

2 RELATED WORK

There are some approaches that aim to move tradi-

tional desktop-based computing to cloud-based solu-

tions in medicine.

Cloudwave (Sahoo et al., 2013) is a platform that

provides paralleled algorithms for computing in a car-

diac domain. It supports real-time interaction with

large volumes of electrophysiological signals, and

features signal visualization and a querying function-

ality using an ontology-driven web-based interface.

The CARMEN e-science project (Watson et al.,

2008) is based on a grid computing concept that in-

tegrates heterogeneous resources across virtual orga-

nizations represented by registered laboratories. For

these laboratories a data/metadata storage, a data

analysis service, and a workﬂow enactment is ensured

but laboratories contribute by their own data and data

analysis methods.

Electrocardiographic (ECG) signal analysis us-

ing Linear Discriminant Analysis (LDA) and Sup-

port Vector Machines (SVM) framework is presented

in (Varatharajan et al., 2018). This framework pro-

vides a data storage for wearable sensors transferred

through a 4G network. Once data is transferred to the

data storage it is computed by LDA and SVM meth-

ods and results are transferred to a doctor’s computer

where he/she can see reports.

An IoT-based health-care architecture (Thota

et al., 2018) uses a fog computing approach for col-

lecting data from wireless sensors. The fog comput-

ing relies on the usage of so-called edge device com-

municating with wearable devices via a local network.

Then this edge-device is connected to the cloud solu-

tion via the Internet. Especially in this solution Rasp-

berry Pi is an edge-device that transfers data to the

cloud. Data in the cloud is accessible by health-care

providers.

3 METHODS

3.1 ERP Domain

Event-related potentials (ERP) (Sur and Sinha, 2009)

can be observed in the electroencephalographic

(EEG) signal as small voltages generated by the brain

as a reaction to stimuli. Many ERP experiments fol-

low an oddball paradigm (Garc

ıa-Larrea et al., 1992)

based on stimulation of tested subjects by two types

of auditory or visual stimuli. Oddball experiments

consist of a set of common (non-target) stimuli that

are infrequently interrupted by rare (target) stimuli.

The target stimuli are associated with the P300 wave-

form. Similar principle can also be applied to brain-

computer interfaces (BCIs) as originally proposed

with the P300 speller by (Farwell and Donchin, 1988)

in which the intended action of a user (such as a row

containing the letter that the user wishes to type) rep-

resents the target stimuli, and the task is to detect that

stimulus by means of machine learning (Hoffmann

et al., 2008).

The detection relies on extracting ERPs from the

ongoing EEG while minimizing noise. The common

procedure is based on epoch extraction and ﬁltering

ERP epochs (trials). When aiming at P300 BCI clas-

siﬁcation, each extracted and processed ERP epoch is

evaluated by a binary classiﬁer that is trained to sep-

arate between target and non-target trials. Finally, the

results of classiﬁcation or parameters of the classiﬁer

itself can be stored for further evaluation. (Hoffmann

et al., 2008)

A typical machine learning workﬂow in the ERP

domain can be organized as Fig. 1 depicts, and it can

consist of the following steps:

• Data reading. Either from ﬁlesystem (a binary

ﬁle speciﬁc for the EEG ampliﬁer or a universal

open ﬁle format such as EDF (Kemp and Olivan,

2003)), or in real-time from stream such as Lab

Streaming Layer (LSL) (Kothe, 2014).

• Pre-processing commonly includes several sub-

steps. Subset selection of only relevant EEG

channels reduces data dimensionality. Band-pass

ﬁltering attenuates undesired frequencies such as

line noise at 50 Hz. Epochs extraction splits con-

tinuous EEG into ERP epochs using stimuli mark-

ers. Each epoch has a pre-stimulus part (a time

window before the stimulus onset) and a post-

stimulus part (a time window after the stimulus

onset). Baseline correction is used to align the

ERP epoch around zero e.g. by subtracting aver-

age of the pre-stimulus part from the whole ERP

epoch. Many other methods can be used, e.g.

blind source separation methods such as Indepen-

dent Component Analysis (Luck, 2014).

• Feature extraction. Most common features to be

extracted are time point features and band power

features (Lotte et al., 2018). Discrete wavelet

transform, matching pursuit (Mallat and Zhang,

Cloud Infrastructure for Storing and Processing EEG and ERP Experimental Data

275

Data

reading

EEG data files

EEG on-line stream

Feature extraction

Preprocessing

Classification

Results visualization

Voltage [μV]

Time [ms]

Accuracy: 80 %

Precision: 78 %

Recall: 82 %

EEG channel

selection

Band-pass

filtering

Epoch

extraction

Baseline

correction

Figure 1: Simple machine learning workﬂow suitable for the P300 ERP detection.

1993) and other related methods for time fre-

quency analysis can also be applied. Feature se-

lection that usually aims at reducing data dimen-

sion by omitting redundant time points or band

power features (Lotte et al., 2018).

• Classiﬁcation. Some methods used in the ERP

domains are e.g. Support Vector Machines

(SVM) (Subasi and Gursoy, 2010), decision

trees (Polat and G

unes¸, 2007), logistic regression

(Subasi and Ercelebi, 2005), random forest (Frai-

wan et al., 2012), or neural networks (G

uler et al.,

2005).

• Results visualization includes charts, tables or

single values.

3.2 System Requirements

The experimental workﬂow outlined in Section 3.1

deﬁnes requirements for the developed system. Data

must be collected in a robust and secured data stor-

age. Processing of data is a sequence of mostly lin-

early executed operations but e.g. classiﬁcation that

uses deep learning methods is required to be paral-

leled. A cloud-based solution is suitable for classiﬁer

training because it is usually the most time consum-

ing part of the workﬂow. However, the data is usually

collected on a local laboratory notebook connected ei-

ther to the Internet or at least to a local laboratory net-

work. Hence a secured data transfer from the labora-

tory notebook to the cloud solution must be ensured.

Data must be anonymized and the server located in-

side a protected network secured by ﬁrewalls.

3.3 Apache Hadoop and Spark

Commonly used open source technologies for data

storing and data processing in distributed environ-

ment include Apache Hadoop and Apache Spark.

Apache Hadoop (Apache Software Foundation, 2009)

is designed for performing massive, parallel jobs and

is de facto supposed as a place where data and com-

putational resources are shared and accessed. It is

an ecosystem of several technologies (Ishwarappa and

Anuradha, 2015). We used a Hadoop Distributed File

System (HDFS) that provides high-throughput access

to data stored in a distributed environment.

Hadoop provides a map-reduce implementation

for processing big data in the distributed environ-

ment (Ekanayake et al., 2008). However, Apache

Spark (Zaharia et al., 2016) reports signiﬁcantly bet-

ter performance than the map-reduce implementation

(Gopalani and Arora, 2015). Moreover, an open-

source distributed machine learning library (MLlib)

(Meng et al., 2016) available for Apache Spark pro-

vides an implementation of various methods suitable

for training classiﬁers in the ERP domain.

ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health

276

4 RESULTS

4.1 Server

The server operates HDFS for experimental data, a

data analysis package described in Section 4.2, and a

Spring boot (Webb et al., 2013) application that oper-

ates a REST API described in Section 4.3.

Once a processing of data is required, a job on the

server is created. The server handles all jobs in a job

manager. Once a job is done, a ﬂag FINISHED is set

up and results are stored on the server. This approach

does not require to wait for every single job but allows

users to run any number of jobs in a row and see the

results once they are available.

ERP experiments (see Section: 3.1) require train-

ing of classiﬁers reusable for similar kinds of exper-

iments. Typically, same experiment is repeated with

various tested subjects. The server allows users to ei-

ther train a classiﬁer on selected data or test a classi-

ﬁer already trained and saved on the server.

The server is controlled by a GUI via the REST

API described in Section 4.4.

Because installing a server with all needed tools

is a difﬁcult task and the server is supposed to be eas-

ily transferred to other environments, we used Docker

(Merkel, 2014). There are several Hadoop Docker im-

ages (e. g. (Vohra, 2015)). We used Cloudera Quick-

start (Taylor, 2010) that we extended by the tools we

implemented (described later in Sections 4.2 and 4.3).

A complete architecture is shown in Figure 2.

4.2 Methods Library

An aim of the methods library is providing the signal

processing tools needed for training and classifying

the ERP signal. We implemented three packages of

tools for (1) data reading and transformation, (2) fea-

tures extraction and (3) classiﬁcation. The Apache

Spark API enables parallelism of the processes over

available nodes. This feature is useful especially for

deep learning methods. The methods library also con-

tains a pipeline builder responsible for running meth-

ods in a workﬂow as described in Section 3.1. The

Apache Spark distributes data and computational load

over nodes connected in the HDFS cluster and col-

lects results. Figure 3 shows all methods currently

implemented in the library.

4.3 REST API

The Apache Spark job is a single process conﬁgured

by Spark properties

. We provided a REST API for

https://spark.apache.org/docs/latest/conﬁguration.html

controlling individual Spark jobs from a client com-

puter. The REST API methods are designed to allow

users to run any number of jobs they need, check their

status, and once they are ﬁnished, download results.

Available methods are:

• /jobs/submit/(id)?(qp) - to submit a job with an id.

Gp is a conﬁguration via the Spark properties.

• /jobs/check/(id) - to check the status of a job with

an id

• /jobs/result/(id) - to get the result of a job with an

• /jobs/log/(id) - to get the log of a job with an id

• /jobs/cancel/id - to cancel a job with an id

• /jobs/conﬁguration/(id) to get the conﬁguration of

a job with an id

• /classiﬁers/list - to list all the saved classiﬁers

4.4 Client GUI

The graphical user interface is only a tool supposed

to be installed locally on a laboratory notebook. The

GUI implements a wizard through which the user can

upload a ﬁle or a complete folder, or delete existing

ﬁles or folders on the server. Once the data is up-

loaded the user can choose data for classiﬁer train-

ing or testing. In the next step, the user lists avail-

able methods on the server, selects one and conﬁg-

ures its parameters. Finally, the REST API method

is called, data from the HDFS storage is submitted to

the Spark, and a Spark job is created and executed. A

print-screen of the GUI is in Figure 4. A complete se-

quence of steps from storing data to obtaining results

is in a sequence diagram in Figure 5. Communica-

tion between the client and the server is secured by

Kerberos (Neuman and Ts’o, 1994).

4.5 System Security

The server itself operates a daily updated Debian

Linux distribution. It is located on a secured univer-

sity network protected by the ﬁrewall. Communica-

tion with the server is secured by Kerberos and the

SSL channel. Moreover, all datasets are anonymized

before uploading. The datasets were anonymized by

using identiﬁcation numbers and neither potential at-

tackers nor experimenters themselves would be able

to assign them to any real person. Since the server it-

self is secured and datasets are anonymized, raw EEG

data encryption was not necessary.

Cloud Infrastructure for Storing and Processing EEG and ERP Experimental Data

277

Figure 2: Architecture of the system. A cloud part of the architecture is a docker image containing on the left side: A Hadoop

distributed ﬁle system (HDFS) implemented in the Cloudera platform. On the right side: The remote server operating the data

analysis package. The server accesses data via a data reader and communicates with the client GUI program via the REST

API.

Figure 3: UML Diagram. Wavelet transformation implements the IFeatureExtraction interface. The IClassiﬁer interface is

implemented by several classiﬁers including random forest, SVM, logistic regression and neural networks. A ﬂow of the data

through the code is controlled by the Pipeline package.

Figure 4: Graphical User Interface

5 DISCUSSION

We have been collecting ERP experiments in a neu-

roinformatics laboratory at our department, in a hos-

pital university and also outside using portable de-

vices. Between autumn 2014 and spring 2015

Figure 2: Architecture of the system. A cloud part of the architecture is a docker image containing on the left side: A Hadoop

distributed ﬁle system (HDFS) implemented in the Cloudera platform. On the right side: The remote server operating the data

analysis package. The server accesses data via a data reader and communicates with the client GUI program via the REST

API.

Figure 3: UML Diagram. Wavelet transformation implements the IFeatureExtraction interface. The IClassiﬁer interface is

implemented by several classiﬁers including random forest, SVM, logistic regression and neural networks. A ﬂow of the data

through the code is controlled by the Pipeline package.

Figure 4: Graphical User Interface.

5 DISCUSSION

We have been collecting ERP experiments in a neu-

roinformatics laboratory at our department, in a hos-

pital university and also outside using portable de-

vices. Between autumn 2014 and spring 2015

we measured experiments at primary and secondary

schools for a science popularization event. Ten times

during that period, we attended various schools and

measured about 20 to 40 tested subjects per day. Later

in 2018, we attended the University hospital in Pilsen

about 10 times where we measured 2-3 tested subjects

per visit. The laboratory experiments from the same

time period consist of about 10 days of experiments

with about 20 tested experimental sessions per day.

The typical experiment we performed at schools is

a ’Guess the number’ experiment. The tested subject

is asked to choose a number from a range 1-9 (the tar-

get stimulus). He/she is then visually stimulated with

a monitor with randomly presented numbers from the

ICT4AWE 2019 - 5th International Conference on Information and Communication Technologies for Ageing Well and e-Health

278

Figure 5: Sequence Diagram.

same range. The goal of the experimenter is to guess

the number based on observing the averaged wave-

forms. The P300 waveform is supposed to appear

following the target stimulus. Finally, we also asked

the participants to reveal the number thought and that

number was recorded as a part of metadata so that the

labels for classiﬁer training were available. Details

about the ’Guess the number’ experiment as well as

the experimental data are described in (Moucek et al.,

2017).

We uploaded the datasets to the cloud infrastruc-

ture by the developed GUI. More than 400 experi-

ments have been stored (tens of gigabytes). Then

we prepared a bash script calling the REST API for

obtaining the datasets and running a simple machine

learning workﬂow. A part of the data was used for

training and another part for testing. For testing data,

classiﬁcation accuracy was between 60-70 %. How-

ever, achieving high classiﬁcation accuracy was not

the main aim of this paper. Classiﬁcation accuracy is

strongly dependent on how the classiﬁers are conﬁg-

ured, how the testing/training data are selected, and

also on the quality of datasets themselves. The most

important was validation of the complete infrastruc-

ture. We validated that the system is suitably inte-

grated, the data is incorruptibly stored, and classiﬁca-

tion gives reasonable results in reasonable time even

when tested on a single cluster. If more performance

is needed, a new node to the infrastructure can be

added without changing the presented system.

6 CONCLUSIONS

Increasing performance and availability of cloud so-

lutions open new possibilities for storing, processing

and visualization of experimental data. Cloud com-

puting concepts allow users to be more focused on

laboratory work without being worried about main-

taining complex data infrastructures. The usage of

cloud based approaches also relies on the researchers

willingness to entrust sensitive data to third-party

providers. The cloud solutions must be secured, re-

liable and easy-to-use to be accepted by researchers.

There are some existing cloud solution providers such

as Microsoft Azure, IBM Cloud or iCloud but they do

not provide sufﬁcient means to satisfy speciﬁc needs

of the electrophysiology domain. Some existing solu-

tions were presented in Section 2. However, they are

not focused on the ERP domain and they are intended

to be used mainly in hospitals or in a clinician prac-

tice. However, there has been no solution for research

in laboratories.

We presented a system based on open source con-

cepts using Apache Hadoop and Spark. We used

HDFS for data storing and we implemented the meth-

ods for signal processing in the Spark context. The

methods are controlled by the REST API. We tested

this infrastructure by storing more than 400 datasets

into the distributed ﬁle system and by running simple

workﬂows aiming at training and testing classiﬁers.

The presented GUI provides a functionality for

Cloud Infrastructure for Storing and Processing EEG and ERP Experimental Data

279

data management and for controlling the available

methods. Moreover if a laboratory notebook operates

some open source tool for collecting data such as e.g.

Neurolab or LabRecorder it can be integrated with the

system using the REST API.

Our future work includes e.g. an implementation

of a workﬂow designer that is supposed to be a com-

fortable graphical tool for designing signal processing

workﬂows (as described in Section 3.1). Data running

through these workﬂows could be processed in the

presented system, too. There also exist micro devices

such as smart phones, smart watches or other wear-

able devices as heart belts, insulin pumps etc. Storing

data from these micro devices in the presented system

could be ﬁnally helpful in building complete Internet

of Things (IoT) infrastructures. Our future goal is also

extending the methods library to provide a larger col-

lection of methods.

ACKNOWLEDGEMENTS

The work was supported by the program INTER-

REG V-A (CZ-BY) Cross-border Cooperation Pro-

gram Czech Republic - Free State of Bavaria un-

der the project No. 85 Brain-driven computer assis-

tance system for people with limited mobility and the

project LO1506 of the Czech Ministry of Education,

Youth and Sports under the program NPU I. Also spe-

cial thanks go to Dorian Beganovic who implemented

most of the code as a part of the Google Summer of

Code 2017 project. Codes for the methods library are

available in (Jezek and Beganovic, 2019a), the client

GUI in (Jezek and Beganovic, 2019b) and the server

in (Jezek and Beganovic, 2019c).

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