Signal Processing - November 2016 - 124

create a brain-computer interface (BCI)
to control robot motion in one dimension.
We use a commercial 14-channel EEG
electrode array from Emotiv, portions of
the BCI2000 framework, and MATLAB
as the programming language. The stu-
dents are guided through the algorithm
development to measure the average sig-
nal power in 2-Hz bandwidths for the 14
electrodes and to build a classifier to
detect a power change in the "best" chan-
nel/frequency pairs.
We start with the clinical data provid-
ed with the BCI2000 framework collected
during an experiment where the subject
was asked to raise his legs or to relax. The
clinical data file contains the raw data
from the EEG sensors as well as the tim-
ing information of the action and rest tri-
als. The BCI2000 framework also
provides the MATLAB functions to
extract the raw data and timing informa-
tion from the file. The students then
implement the signal processing and

The first project is acoustical
source localization using a four-element
microphone array [2]. The students are
guided through an algorithm for triangu-
lating a source location using a four-ele-
ment microphone array. Next, they
develop the signal processing implemen-
tation using LabVIEW as the program-
ming language and a simulator that we
provide them. Once the triangulation
works in simulation, they test their imple-
mentation on the actual acoustic array. In
the next phase of the project, the students
implement the triangulation using a cen-
tral computer and two modified National
Instruments Dani Robots, each equipped
with Wi-Fi and a microphone pair as
shown in Figure 1(a). In their final presen-
tation, we move a chirping speaker
around and their mobile robots automati-
cally track it.
The second project in this course is a
multidisciplinary electroencephalogram
(EEG) signal processing application to

(a)

(b)

Figure 1. Platforms used in ESE297: (a) the mobile microphone array and (b) a student controls a
robot with EEG signals from the Emotiv headset.

124

IEEE Signal Processing Magazine

November 2016

classification in MATLAB working on
the clinical data. After they validate their
implementation against the BCI2000
results for the same data, they collect their
own data with the Emotiv headset while
being prompted to clench their fists or
rest. Using their collected data as the
training set, the students design their own
classifier using their "best" channel/fre-
quency pairs. When they are satisfied
with the receiver operating characteristic
(ROC) curve of their system, they design/
implement a real-time LabVIEW/MAT-
LAB/Emotiv software development kit
(SDK) application that processes the
EEG data in real time and moves the
mobile robot every time they clench their
fists as shown in Figure 1(b) [3].

ESE497: Undergraduate
Research Projects
The top students from ESE297 are
encouraged to sign up for the course
Undergraduate Research Projects
(ESE497) [4]. However, ESE297 is not
necessarily a prerequisite for ESE497. We
also encourage the top third- and fourth-
year students from across the engineering
school to get involved in our undergradu-
ate research projects. The students are
required to work on their projects for at
least ten hours per week for two credits,
create a blog/webpage of their project,
and participate in several poster sessions.
The required presentations are a good
experience for the students, where feed-
back is provided from their peers as well
as from the faculty. Where applicable, we
encourage our students to publish their
projects in a peer-reviewed journal.
Students are given a choice of 50 proj-
ects [5] listed on our website that cover
topics such as signal and image process-
ing, machine learning, optimization,
power and energy, controls and opera-
tions research. We interview each student
to find a good match between his or her
interest and the available projects. One of
the themes of our projects is multidisci-
plinary robotic sensing [6], merging sig-
nal and image processing, robotics, and
biomedical and environmental sensing.
We are also flexible when an entrepre-
neurial-minded student comes to us with
a good idea. Sometimes the student's
ideas/projects are part of our design

Table of Contents for the Digital Edition of Signal Processing - November 2016