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Taverna (Taverna, 2013) is an open source and
domain-independent Workflow Management System
– a suite of tools used to design and execute
scientific workflows.The Taverna suite is written in
Java and includes the Taverna Engine (used for
enacting workflows) that powers both the Taverna
Workbench (the desktop client application) and the
Taverna Server (which allows remote execution of
workflows). Taverna is also available as
a Command Line Tool for a quick execution of
workflows from a terminal (Taverna, 2013).
e-Science Central is a Cloud based Platform for
Data Analysis. It supports secure storage and
versioning of data, audit and provenance logs and
processing of data using workflows. Workflows are
composed of blocks which can be written in Java, R,
Octave or Javascript (eScience, 2013). Scientists are
able to design workflows using the drag-and-drop
online workflow designer by selecting blocks
(services). The input and output of each block is
typed to prevent incompatible blocks being
connected to each other (Watson, et al. 2010).
2.2 Syntactic Compatibility of
Workflows
The engines described above are designed for
scientific purposes. They provide modelling of
workflows in many scientific areas including
neuroinformatics and in the domain of electro-
physiological experiments.
All of the mentioned engines use the parameter
type control during data processing (Stebetak, 2013).
This simple comparison of parameters ensures that
only compatible methods can be connected.
However, methods used in the electrophysiology
domain are specific in case of syntax and semantics
for various inputs/outputs. For example, only a
subset of the result of a previous method can be used
as an input to a next method. This case is not solved
by these engines.
For well-designed workflows, ensuring syntac-
tical compatibility is necessary but not a single step.
Used methods have to be also connected correctly in
terms of their semantics. However, semantics of
piped methods (if the connection makes sense or
not) is not satisfactorily solved by these engines.
3 ANALYTIC METHODS AND
ALGORITHMS
The following subsections briefly describe a set of
methods suitable for EEG/ERP signal analysis.
These methods are used for detection of ERP
waveforms or artifact removal.
3.1 Signal Preprocessing
A pure EEG signal contains a lot of artifacts (non-
cerebral signal); ERP waveforms are hidden.
Therefore, signal preprocessing methods are used for
suppressing artifacts and obtaining ERP waveforms.
An EEG signal is divided into epochs. Each
epoch starts at the time when a stimulus appeared
and its length depends on the latency and length of
ERP waveforms. In ERP experiments, several types
of stimuli are used.
Averaging (Rondik, 2012) is a common method
for highlighting ERP waveforms. Since the
background EEG has a higher amplitude then ERP
waveforms, the averaging technique highlights the
waveforms and suppress the background EEG
(Vidal, 1977). A set of epochs is the input of the
averaging method. The output of this method is an
averaged signal belonging to a specific stimulus.
3.2 Signal Processing
We widely use the following signal processing
methods: Fast Fourier transform, Matching Pursuit,
Discrete and Continuous Wavelet transform, ICA,
and Hilbert-Huang transform (Ciniburk, et al. 2010).
This section briefly describes principles of these
algorithms.
The Fourier transform converts waveform data in
the time domain into the frequency domain. Since
artifacts usually have higher amplitude and
frequency than a normal ERP component, this
technique is useful for detecting artifacts within the
EEG or ERP signal.
The matching pursuit (MP) algorithm is
frequently used for continuous EEG processing. It
decomposes any signal into a linear expansion of
functions called atoms. An input signal is
approximated by a Gabor atom, which has the
highest scalar product with the original signal, and
then it is subtracted from the signal. This process is
repeated until the whole signal is approximated by
Gabor atoms with an acceptable error (Vareka,
2012).
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
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