{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T03:00:46Z","timestamp":1692500446462},"reference-count":2,"publisher":"Walter de Gruyter GmbH","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Brain-Computer Interfacing (BCI) is a promising technology for patients that are severely motor-disabled, because it\nenables them to communicate and interact with the environment. A BCI system decodes user's intentions from brain signals,\ntypically recorded with electroencephalography (EEG), and transmits them to a computer application that, e.g., controls\na wheelchair. The efficiency of the system largely depends upon a reliable extraction of informative features from the\nhigh-dimensional EEG signal. Spatial filtering is a crucial step in this protocol, however, current approaches are prone\nto errors when data is contaminated by artifacts or is nonstationary. This article provides an overview of a dissertation,\nwhich has addressed the problem of robust spatial filtering in BCI. The contributions of the thesis range from the\ndevelopment of regularization schemes and a robust parameter estimator for spatial filtering, to the formulation of an\ninformation geometric view on the spatial filtering problem and the proposal of a new family of algorithms based on robust\ndivergences. The developed methods and concepts are applicable to a variety of problems in machine learning and signal\nprocessing.<\/jats:p>","DOI":"10.1515\/itit-2016-0023","type":"journal-article","created":{"date-parts":[[2016,6,27]],"date-time":"2016-06-27T15:45:02Z","timestamp":1467042302000},"page":"150-154","source":"Crossref","is-referenced-by-count":4,"title":["On robust spatial filtering of EEG in nonstationary environments"],"prefix":"10.1515","volume":"58","author":[{"given":"Wojciech","family":"Samek","sequence":"first","affiliation":[{"name":"Fraunhofer Heinrich Hertz Institute, Department of Video Coding & Analytics, D-10587 Berlin, Germany"}]}],"member":"374","published-online":{"date-parts":[[2016,6,25]]},"reference":[{"key":"ref141","first-page":"165","article-title":"Cambridge , MA : MIT Press Linear and Non - Linear Methods for Brain - Computer Interfaces Neural Syst Rehabil","volume":"11","author":"M\u00fcller","year":"2007","journal-title":"IEEE Trans Eng"},{"key":"ref171","article-title":"vol of Transl of Math Monogr American Math Divergence - based Framework for Common Spatial Patterns Algorithms","author":"Samek","year":"2000","journal-title":"Soc IEEE Rev"}],"container-title":["it - Information Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0023\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0023\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T11:45:03Z","timestamp":1624448703000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0023\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,25]]},"references-count":2,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,6,25]]},"published-print":{"date-parts":[[2016,6,28]]}},"alternative-id":["10.1515\/itit-2016-0023"],"URL":"https:\/\/doi.org\/10.1515\/itit-2016-0023","relation":{},"ISSN":["2196-7032","1611-2776"],"issn-type":[{"value":"2196-7032","type":"electronic"},{"value":"1611-2776","type":"print"}],"subject":[],"published":{"date-parts":[[2016,6,25]]}}}