Decoding intracranial EEG data with multiple kernel learning method
详细信息 查看全文
- 作者：Jessica Schrouffa ; b ; Janaina Mourã ; o-Mirandab ; Christophe Phillipsc ; Josef Parvizia ; jparvizi@stanford.edu" class="auth_mail" title="E-mail the corresponding author ; jschrouf@stanford.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：Multiple kernel learning ; Intracranial EEG ; Machine learning ; Feature selection
- 刊名：Journal of Neuroscience Methods
- 出版年：2016
- 出版时间：1 March 2016
- 年：2016
- 卷：261
- 期：Complete
- 页码：19-28
- 全文大小：2607 K
文摘
- class="listitem" id="list_lis0005">
- class="label">•
Multiple Kernel Learning (MKL) can explore multiple dimensions simultaneously.
- class="label">•
MKL method is sparse and performs feature selection during modeling of the data.
- class="label">•
We propose to use the MKL method to analyze electrophysiology data (EEG or MEG).
- class="label">•
We provide a prototype example of how MKL method can apply to ECoG data.
- class="label">•
We show multiple dimensions of ECoG signal can contribute to numerical processing.