Information Technology Reference
In-Depth Information
7.6
Discussion
Many EEG signal-processing tools are available in order to classify EEG signals
into the corresponding user
'
is mental state. However, EEG signal processing is a
very dif
cult task, due to the noise, non-stationarity, complexity of the signals as
well as due to the limited amount of training data available. As such, the existing
tools are still not perfect, and many research challenges are still open. In particular,
it is necessary to explore and design EEG features that are (1) more
informative
,in
order to reach better performances, (2)
robust
, to noise and artifacts, in order to use
the BCI outside laboratories, potentially with moving users, (3)
invariant
, to deal
with non-stationarity and session-to-session transfer and (4)
universal
, in order to
design subject-independent BCI, i.e., BCI that can work for any user, without the
need for individual calibration. As we have seen, some existing tools can partially
address, or at least, mitigate such problems. Nevertheless, there is so far no EEG
signal-processing tool that has simultaneously all these properties and that is per-
fectly robust, invariant, and universal. Therefore, there are still exciting research
works ahead.
7.7
Conclusion
In this chapter, we have provided a tutorial and overview of EEG signal-processing
tools for users
mental-state recognition. We have presented the importance of the
feature extraction and classi
'
cation components. As we have seen, there are
three main sources of information that can be used to design EEG-based BCI:
(1) the spectral information, which is mostly used with band-power features; (2) the
temporal information, represented as the amplitude of preprocessed EEG time
points, and (3) the spatial information, which can be exploited by using channel
selection and spatial
filtering (e.g., CSP or xDAWN). For BCI based on oscillatory
activity, the spectral and spatial information are the most useful, while for ERP-
based BCI, the temporal and spatial information are the most relevant. We have also
brie
y explored some alternative sources of information that can also complement
the 3 main sources mentioned above.
This chapter aimed at being didactic and easily accessible, in order to help
people not already familiar with EEG signal processing to start working in this area
or to start designing and using BCI in their own work or activities. Indeed, BCI
being such a multidisciplinary topic, it is usually dif
cult to understand enough of
the different scienti
c domains involved to appropriately use BCI systems. It should
also be mentioned that several software tools are now freely available to help users
design BCI systems, e.g., Biosig (Schl
gl et al.
2007
), BCI2000 (Mellinger and
Schalk
2007
) or OpenViBE (Renard et al.
2010
). For instance, with OpenViBE, it is
possible to design a new and complete BCI system without writing a single line of
code. With such tools and this tutorial, we hope to make BCI design and use more
accessible, e.g., to design brain-computer music interfaces (BCMI).
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