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EEG Subspace Analysis and Classifica...

Ashari, Rehab Bahaaddin.

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EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces./
作者:	Ashari, Rehab Bahaaddin.
面頁冊數:	98 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-10(E), Section: B.
Contained By:	Dissertation Abstracts International76-10B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3706329
ISBN:	9781321800272

EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces.
Ashari, Rehab Bahaaddin.

EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces. - 98 p.

Source: Dissertation Abstracts International, Volume: 76-10(E), Section: B.

Thesis (Ph.D.)--Colorado State University, 2015.

Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences.

ISBN: 9781321800272Subjects--Topical Terms:

523869
Computer science.

EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces.
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520 $a Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences.
520 $a This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations.
520 $a One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only, should make this approach more appropriate for online BCI applications.
520 $a In order to understand and test how EEG signals are different from one subject to another, different users are tested in this dissertation, some with motor impairments. Furthermore, the concept of transferring information between subjects is examined by training the approach on one subject and testing it on the other subject using the training subject's EEG subspaces to classify the testing subject's trials.
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