Information Technology Reference
In-Depth Information
7.8
Questions
Please
find below 10 questions to re
ect on this chapter and try to grasp the
essential messages:
1. Do we need feature extraction? In particular why not using the raw EEG
signals as input to the classi
er?
2. What part of the EEG signal-processing pipeline can be trained/optimized
based on the training data?
3. Can we design a BCI system that would work for all users (a so-called subject-
independent BCI)? If so, are BCI designed speci
cally for one subject still
relevant?
4. Are univariate and multivariate feature selection methods both suboptimal in
general? If so, why using one type or the other?
5. By using an inverse solution with scalp EEG signals, can I always reach a
similar information about brain activity as I would get with invasive
recordings?
6. What would be a good reason to avoid using spatial
filters for BCI?
7. Which spatial
filter to you have to try when designing an oscillatory activity-
based BCI?
8. Let us assume that you want to design an EEG-based BCI, whatever its type:
Can CSP be always useful to design such a BCI?
9. Among typical features for oscillatory activity-based BCI (i.e., band-power
features) and ERP-based BCI (i.e., amplitude of the preprocessed EEG time
points), which ones are linear and which ones are not (if applicable)?
10. Let us assume you want to explore a new type of features to classify EEG data:
Could they bene
t from spatial
filtering and if so, which one?
References
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