|Publication type:||Book part|
|Type of review:||Peer review (publication)|
|Title:||General principles of machine learning for brain-computer interfacing|
Millán, José del R.
|Published in:||Handbook of Clinical Neurology ; 168|
|Editors of the parent work:||Millan, José del R|
Ramsay, Nick F.
|Publisher / Ed. Institution:||Elsevier|
|Subjects:||Artifact; Brain-computer interface; Brain–machine interface; Classification; Cross-validation; Feature; Filtering; Information transfer rate; Machine learning; Performance evaluation; Regression|
|Subject (DDC):||004: Computer science|
|Abstract:||Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Centre for Artificial Intelligence (CAI) |
Institute of Applied Information Technology (InIT)
|Appears in collections:||Publikationen School of Engineering|
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