Brain–Computer Interfaces - Index of

A Simple, Spectral-Change Based,
Electrocorticographic Brain–Computer
Interface
Kai J. Miller and Jeffrey G. Ojemann
1 Introduction
A brain–computer interface (BCI) requires a strong, reliable signal for effective
implementation. A wide range of real-time electrical signals have been used for
BCI, ranging from scalp recorded electroencephalography (EEG) (see, for example,
[1, 2]) to single neuron recordings (see, for example, [3, 4]. Electrocorticography
(ECoG) is an intermediate measure, and refers to the recordings obtained directly
from the surface of the brain [5]. Like EEG, ECoG represents a population measure,
the electrical potential that results from the sum of the local field potentials
resulting from 100,000 s of neurons under a given electrode. However, ECoG is a
stronger signal and is not susceptible to the artifacts from skin and muscle activity
that can plague EEG recordings. ECoG and EEG also differ in that the phenomena
they measure encompass fundamentally different scales. Because ECoG electrodes
lie on the cortical surface, and because the dipole fields [7] that produce the cortical
potentials fall off rapidly (V(r) ∼ r −2 ), the ECoG fundamentally reflects more local
processes.
Currently, ECoG takes place in the context of clinical recording for the treatment
of epilepsy. After implantation, patients recover in the hospital while they
wait to have a seizure. Often, that requires a week or longer of observation, during
which time patients may chose to participate in experiments relevant to using ECoG
to drive BCI. Recently, researchers have used the spectral changes on the cortical
surface of these patients to provide feedback, creating robust BCIs, allowing individuals
to control a cursor on a computer screen in a matter of minutes [8–12]. This
chapter discusses the important elements in the construction of these ECoG based
BCIs: signal acquisition, feature selection, feedback, and learning.
K.J. Miller (B)
Department of Physics, Neurobiology, and Behavior, University of Washington, Seattle, WA
98195, USA
e-mail: kjmiller@u.washington.edu
B. Graimann et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection,
DOI 10.1007/978-3-642-02091-9_14, C○ Springer-Verlag Berlin Heidelberg 2010
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