Biomedical Engineering Reference
In-Depth Information
7.4.3
Habituation and Learning Effects
In classical analysis of ERPs, averages are performed across large sets of trials and
subjects, ignoring possible fluctuations arising from e.g. attention-related processes,
learning and changes of strategy. Another interesting application of single-trial
analysis is to open the way for a fine characterization of such fluctuations. In [
7
], the
authors showed that it is possible to characterize habituation effects with single-trial
analysis in a simple stimulation protocol. Quian Quiroga and Van Luijtelaar [
20
]
have shown that different waves of the evoked potentials have different profiles
of habituation, some with slow variations and other waves with habituation taking
place in a few trials only. This suggests that these waves are related to different
processes.
7.5
Conclusion
In this chapter, we presented the analysis of multitrial electrophysiology datasets
coming from neuroelectromagnetic recordings by EEG and MEG. Such measure-
ments present several characteristics: the absence of ground-truth data, and a high
level of noise, which can be defined as the part of the data which is uncorrelated
across trials. Multitrial recordings are compulsory, in order to extract meaningful
information from the data. Moreover, the information of interest is subject to
inter-trial variability. This chapter has focussed on two families of data processing
methods that are applied in this context: data-driven methods, in a section on
non-linear dimensionality reduction, and model-driven methods, in a section on
Matching Pursuit and its extensions. The importance of correctly capturing the inter-
trial variability is underlined in the last section which presents three case-studies in
clinical and cognitive neuroscience. Furthermore, the rapidly growing field of Brain
Computer Interfaces is driving research on the online interpretation of EEG signals,
and we can therefore expect much progress on single-trial biosignal analysis in the
years to come.
7.6
Online Resources
7.6.1 Datasets
BCI Competitions
http://www.bbci.de/competition/
This site provides EEG/MEG/ECoG datasets for the purpose of comparing signal
processing and classification methods for Brain Computer Interfaces.
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