
Jinyin Zhang
Carnegie Mellon University

A Doubly Regularized Support Vector Machine for Automatic Channel Selection of Brain Computer Interface
In a realtime braincomputer interface (BCI), it is
desirable to minimize the number of recording channels
so that the system complexity (e.g., electrode number,
data rate, communication bandwidth, signal processing
hardware, etc.) can be reduced. While such a channel
selection is a critical component to enable realtime
BCI, it has not been extensively studied in both communities
of neuroscience and machine learning. In particular,
most algorithms developed for feature selection are
not applicable here, because one channel is typically
mapped to multiple features and, hence, simply minimizing
the number of features does not necessarily result in
the minimal number of channels. In this work, we propose
a novel doubly regularized support vector machine (DrSVM)
to address the aforementioned problem. Our objective
is to achieve a highquality classifier for movement
decoding, while simultaneously minimizing the number
of channels that are used. The proposed DrSVM is derived
from the theory of sparse classifiers (in particular,
L1norm SVM) and is carefully tuned for BCI application.
Most importantly, we develop the concept of doubly regularized
SVM by introducing a set of additional variables to
model the "importance" of every channel. These
extra variables are subject to sparsity constraints
so that solving the proposed DrSVM results in a minimal
number of important channels. In addition, while the
DrSVM formulation cannot be mapped to a convex programming,
we propose a robust relaxation algorithm to find the
optimal model coefficients. Such a relaxation algorithm
consists of a sequence of linear programming steps and
guarantees to converge to a local optimal point.
The proposed DrSVM was tested by several experiments
using Magnetoencephalography (MEG) of human subjects.
Our preliminary results show that compared with the
traditional L1norm SVM, the proposed DrSVM reduces
the number of selected channels by 5~20x, while achieving
the same accuracy for classification.
Bio
Jinyin Zhang received her B.S and M.S degrees in Computer
Science from Beijing University of Astronautics and
Aeronautics in 2002 and 2005, respectively. From 2005
to 2007, she worked for Lucent Technologies China as
an engineer in base station software development. She
is currently working towards the Ph.D. in the department
of Electrical and Computer Engineering at Carnegie Mellon
University.
