Brain–Computer Interfaces - Index of

A Simple, Spectral-Change Based, Electrocorticographic Brain–Computer Interface 245
common-mode phenomena. One must be careful that there are not any electrodes
that are broken, or have extremely large contamination, or every electrode will be
contaminated by the re-referencing process. Local re-referencing may also be performed,
such as subtracting the average of nearest-neighbors (Laplacian), which
ensures that the potential changes seen in any electrode are spatially localized. One
may also re-reference in a pair-wise fashion, producing bipolar channels which are
extremely local, but phenomena cannot be tied to a specific electrode from the pair.
This re-referencing can also be interpreted as applying a spatial filter. Please see
Chapter “Digital Signal Processing and Machine Learning” for details about spatial
filters.
In order to appropriately understand both the experimental context and the
connection between the structure of the brain and signal processing findings, it
is necessary to co-register electrode locations to the brain surface. The simplest
method is to use x-rays, and plot data to template cortices (as illustrated in Fig. 2).
A clinical schematic will typically be obtained from the surgeon. The position of
each electrode may then be correlated with potential recordings from each amplifier
channel. Different diagnostic imaging may be obtained from the course of the clinical
care, or through specially obtained high-fidelity experimental imaging. The level
and quality of this may be highly variable across time and institutions, from x-ray
only, to high-fidelity pre-operative magnetic resonance imaging (MRI) and postoperative
fine-cut computed tomography (CT). The clinical schemata and diagnostic
imaging may be used in concert to estimate electrode positions, recreate cortical
locations, and plot activity and analyses. The most simple method for doing this,
using x-rays, is the freely-available LOC package [16], although there is the promise
of more sophisticated methodology for doing this, when higher fidelity diagnostic
imaging is obtained.
Choosing a sampling frequency is important – there is often a trade-off between
signal fidelity and the practical issues of manageable data sizes and hardware limitations.
Whatever sampling rate is chosen, one should be sure to have high signal
fidelity up to at least 150 Hz. This means that the sampling rate should be above
300 Hz, because of a law called the Nyquist Law (aka Nyquist Theorem), which
says that you must record data at (at least) twice the frequency of the highest wave
you wish to measure. The sampling rate may have to be higher if the amplifiers used
have built in filtering properties. The reason for this is that there is a behavioral split
in the power spectrum (see Fig. 3) which can be as high as 60 Hz [17]. In order to
capture the spatially focal high frequency change, one must have large bandwidth
above this behavioral split. Some characteristic properties of motor and imagery
associated spectra are shown in Fig. 3. There is a decrease in the power at lower
frequencies with activity, and an increase in the power at higher frequencies [6, 18–
20]. The intersection in the spectrum is dubbed the “primary junction” (J0). A recent
study involving hand and tongue movement [17] found that, for hand movement,
J0 = 48+/−9 Hz (mean+/− SD) (range 32−57 Hz), and, for tongue movement,
J0 = 40+/−8 Hz (range 26–48 Hz). Rather than this indicating two phenomena, a
“desynchronization” at low frequencies, and a “synchronization” at high frequencies,
as some have proposed [19, 21], this might instead reflect the superposition