کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
405599 677685 2009 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving BCI performance by task-related trial pruning
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Improving BCI performance by task-related trial pruning
چکیده انگلیسی

Noise in electroencephalography data (EEG) is an ubiquitous problem that limits the performance of brain computer interfaces (BCI). While typical EEG artifacts are usually removed by trial rejection or by filtering, noise induced in the data by the subject’s failure to produce the required mental state is very harmful. Such “noise” effects are rather common, especially for naive subjects in their training phase and, thus, standard artifact removal methods would inevitably fail. In this paper, we present a novel method which aims to detect such defected trials taking into account the intended task by use of Relevant Dimensionality Estimation (RDE), a new machine learning method for denoising in feature space. In this manner, our method effectively “cleans” the training data and thus allows better BCI classification. Preliminary results conducted on a data set of 43 naive subjects show a significant improvement for 74% of the subjects.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 22, Issue 9, November 2009, Pages 1295–1304
نویسندگان
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