1. Brain-Computer Interface for control of an on-screen
keyboard
Magani, Pablo Sebastián; Iatzky, Pedro Gastón
Bioengineering Laboratory, Faculty of Engineering, Mar del Plata National University
maganipablo@hotmail.com
gastiatzky@hotmail.com
Abstract—Brain-computer interfaces (BCIs) are technological
devices based on the acquisition of brain signals and their
processing and classification, with the purpose of providing the
user with control over external devices or applications. BCIs are
an attractive option to improve the quality of life of people with
severe motor impairments, since they allow them to communicate
and to send commands to other devices by using their EEG signals.
For this project, a small, low cost circuit was designed for the
acquisition of EEG signals, and it was implemented in a BCI that
allowed the user to control an on-screen keyboard using SSVEPs.
Both the circuit and the software developed yielded excellent
results, proving the project to be an appropriate non-invasive
system for the aid of severely disabled people.
Keywords—BCI, EEG acquisition circuit, speller, SSVEP
I. INTRODUCTION
Many different disorders can disrupt the neuromuscular
channels through which the brain communicates with and
controls its external environment. Amyotrophic lateral sclerosis
(ALS), brainstem stroke, spinal cord injury, multiple sclerosis,
and numerous other diseases impair the neural pathways that
control muscles or impair the muscles themselves. Those most
severely affected may lose almost all (or all) voluntary muscle
control, leaving them unable to communicate in any way.
Brain-computer interfaces (BCIs) provide the user with an
alternative way to send commands and messages to the external
world by using only their electroencephalographic (EEG)
signals. These systems record, process and classify brain
signals in order to translate them into commands, according to
the user’s intentions. This way, a severely disabled subject can
communicate with the external world. For this work, a circuit
for the acquisition of EEG signals was designed and
manufactured, in order to be used in a BCI based on Steady
State Visually Evoked Potentials (SSVEPs) that allows the user
to spell words with an on-screen keyboard. Processing,
classification and graphical interface were developed using
MatLab®
.
II. METHODOLOGY
A. EEG Acquisition Circuit
Our objective was to design and manufacture a small (less
tan 10x10 cm), low cost EEG acquisition circuit with at least 8
input channels. We used an integrated circuit called ADS1299,
manufactured by Texas Instruments Inc., as the main
component of our circuit [1]. Altium Designer®
was used to
design the printed circuit board. In Fig.1 a picture of the printed
circuit board is shown, with components already populated. All
components were hand soldered, using lead paste. The
dimensions of the finished circuit are 5x5 cm. Table 1 shows
the cost of one acquisition circuit, not taking into account the
soldering process. The circuit requires an external digital
controller to be able to communicate with a PC through an USB
port. We used a ChipKit Uno32 board for this purpose.
Fig. 1. EEG acquisition circuit.
TABLE 1
COSTS
Item Cost (USD)
ADS1299 66.00
Other ICs (supply,
etc.)
8.62
Passive components 9.83
PCB 7.35
TOTAL 91.80
B. SSVEP-based speller
SSVEP appear mainly over the primary visual cortex when
a person focuses on a repetitive visual stimulus, such as a
blinking light at a fixed frequency [2]. These evoked potentials
have a greater amplitude on electrode locations Oz, O1, O2 and

2. POz, according to the extended 10-20 standard [3]. The
fundamental frequency of a SSVEP is the same as the
frequency of the visual stimulus that caused it.
The on-screen keyboard mentioned in this work was
developed using a free MatLab toolbox called Psychophysics
Toolbox. In Fig 2. the keyboard is shown. It includes several
special characters, digits from 0 to 9 and the “<” character
which actually works as the backspace key. The numbers from
1 to 4 that are on the sides of and over and below the 40
characters represent the numbering used for four blinking
rectangles. Each of these blinking rectangles blink at a unique
frequency and their purpose is to provide the user with the
stimulus necessary for generating SSVEPs. In order to send
commands to the keyboard, the user must focus on one of the
blinking rectangles and the program must correctly identify on
which one the user was focusing.
Fig. 2. On-screen keyboard. On the right upper corner the text being written by
the user is shown (“QUE”). Numbers 1, 2, 3 y 4 on the sides indicate the
numbering of the blinking rectangles, which are not shown in this image.
The steps required for writing a single character are as
follows:
1. The keyboard is presented on the screen for two
seconds. The user must find the character he/she
wants to select and must memorize on which row
that character is. This step is portrayed by Fig 2.
2. The keyboard disappears. The rectangles start
blinking and the user should focus on the rectangle
that corresponds with the row in which the desired
character was located. After 4 seconds, the
rectangles stop blinking and the keyboard reappears.
3. The classification algorithm analyzes the EEG
signals and determines on which rectangle the user
was focusing, which results in the program
knowing on which row the desired character is.
Then, the first three characters of that row are
highlighted.
4. If the user wants to write one of those three
highlighted characters, he/she must focus on the
corresponding rectangle from the subset {1,2,3}. If
the user wants to write a character that is in that row
but further to the right (i.e., not highlighted), he/she
must focus on the fourth blinking rectangle.
5. The keyboard disappears and the rectangles start
blinking again for four seconds. Then, the keyboard
reappears.
6. The classification algorithm determines on which
rectangle the user was focusing. If the user focused
on a rectangle from the subset {1,2,3}, the selected
character is added to the ones already written on the
right upper corner and the process ends..
7. If the user focused on the fourth blinking rectangle,
the next three characters of the selected row are
highlighted and steps 4, 5 and 6 are repeated.
8. If the user focuses on the fourth blinking rectangle
again, the last four characters of the selected row
are highlighted and the user must now focus on one
of the blinking rectangles, since each one now
corresponds with a highlighted character. The
keyboard disappears and the rectangles start
blinking one last time. The classification algorithm
identifies on which rectangle the user was focusing
and the corresponding character is added to the
ones already written in the right upper corner.
The placement of each character is such that the most used
in the Spanish language are on the leftmost side of the keyboard
[4]. This way the writing speed is maximized, since most of the
time the character that the user wants will be among the first
three of the selected row and they will be highlighted on step 3.
The “<” character was placed on the left side too, so that the
user can rapidly correct a spelling error.
C. Processing
Let a trial be each instance of the program in which the
rectangles blink, EEG signals are recorded and then processed
and classified. Input signals come from four channels,
corresponding to four electrodes placed on Oz, O1, O2 and POz,
all referred to the left earlobe. The sampling frequency is 250
Hz. On each trial, 1024 samples are taken from each channel
for processing and classification. The result of each trial is a
single number from 1 to 4, which corresponds to one of the four
blinking rectangles.
EEG data is digitally filtered using a combined filter,
resulting from the discrete convolution of two other FIR filters.
One is a passband filter with a passband from 5 to 38 Hz, the
other one is an optimum notch filter at 50 Hz (which is the line
frequency in Argentina) [5]. The rectangles blinked at
frequencies between 9 to 20 Hz so as to evoke SSVEPs of
greater amplitude.
Since the classification algorithm’s function is to determine
on which of the rectangles the user was focusing and since
SSVEPs have a fundamental frequency that is equal to the
stimulus that evoked them, it is logical to think that the spectral
densities of the EEG signals carry the required information. The
Fast Fourier Transform (FFT) was tried first to calculate the
periodogram of each channel. However, we found that
computing the FFT using all 1024 samples of a trial did not
reveal the spectral peak that is characteristic of SSVEPs, but
computing it with, for example, the first 512 samples of the
same trial would then show a spectral peak. This could happen
because the oscillation in the EEG signal evoked by the
blinking rectangle is not present during the whole duration of
the trial. The cause of this could be attributed to factors that are
out of our control and cannot be predicted, such as brief
interferences, artifacts or momentary distractions of the user.
A different method of estimating the spectral density was
tried, called Welch’s method [6]. When using Welch’s method
to estimate a spectral density, the input samples are divided into
segments with a defined overlap. A Hamming windowing

3. function is applied on each segment (in the temporal domain)
and then the FFT of each segment is calculated. The resulting
values are squared to obtain various periodogramas, and these
are then averaged. This reduces the variance of the individual
periodograms and the aforementioned problem disappears. In
Fig. 3 the result of a spectral density estimation using Welch’s
Method is shown. The user was focusing on a 10 Hz blinking
rectangle and the EEG signal was recorded from an electrode
placed on location Oz. An inherent drawback when using
Welch’s method is a lower frequency resolution, since the
individual FFTs are computed using less data points. As a
consequence, the frequency of each of the blinking squares
must differ from the others by at least twice the frequency
resolution obtained.
Fig. 3. Spectral density estimation using Welch’s method. The peak at 10 Hz is
consistent with the frequency of the blinking rectangle the user was focusing
on during this trial.
D. Classification Algorithm
As mentioned before, the purpose of classification is to
determine, for each trial, on which blinking rectangle the user
was focusing. The inputs to the classification block of the
program are four spectral density estimations, computed using
Welch’s method on the samples taken from the four electrodes
placed over the visual cortex. These spectral density
estimations will be named𝑃1(𝑓), 𝑃2(𝑓), 𝑃3(𝑓) and 𝑃4(𝑓).
Given a set of frequencies 𝐹 = {𝑓1, 𝑓2, 𝑓3, 𝑓4}, in which 𝑓𝑛 is
the frequency at which the rectangle n blinks, a score vector is
defined as 𝑆 = [𝑠1, 𝑠2, 𝑠3, 𝑠4], where 𝑠 𝑛 is the classification
score for the blinking rectangle n. This vector is set to all zeroes
in the beginning of each trial. Then, [𝑖, 𝐴𝑗] = max
𝑖
{𝑃𝑗(𝑓𝑖)} is
computed for each 𝑃𝑗, where 𝑖 is an integer number from 1 to 4
that indicates which of the frequencies 𝑓𝑖 in set F gives the
highest amplitude 𝐴𝑗 in spectral density estimation 𝑃𝑗. Let ∆𝑓
be the frequency range of interest (from 8 to 20 Hz) so that the
SNR (Signal to Noise Ratio) of each estimation j is defined as
𝑆𝑁𝑅𝑗 =
𝐴𝑗
𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑜𝑓 𝑃𝑗 𝑖𝑛 𝑡ℎ𝑒 ∆𝑓 𝑟𝑎𝑛𝑔𝑒
If the 𝑆𝑁𝑅𝑗 value is greater than a certain threshold Ω 𝑠𝑖𝑛𝑔𝑙𝑒 .
it is then added to the corresponding score 𝑠𝑖 from score vector
S. After calculating the four 𝑆𝑁𝑅𝑗 and assigning the
appropriate scores to the S vector, the classification algorithm
determines that the user was focusing on the blinking square
that had the highest score, but only if this final score is also
greater than another threshold, Ω 𝑔𝑟𝑜𝑢𝑝. These two thresholds
were tuned to arbitrary values that yielded the best empirical
results. If none of the elements of S exceed the Ω 𝑔𝑟𝑜𝑢𝑝
threshold, the user is requested to repeat the trial by means of
an on-screen message that reads “REPEAT”.
III. RESULTS
A. Performance of the acquisition circuit
To measure the performance of the acquisition circuit
designed, a self-noise measurement was made. This
measurement involves creating a short-circuit between the
differential input pints and short them to ground. Since the
input signal is zero, the output of the system will be noise
generated by the circuit itself.
In Fig. 4 a measurement of self-noise is shown. Gain was set
to 24 (higher gain lowers the output noise in the ADS1299) and
data rate was set to 250 samples per second. The standard
deviation 𝜎 𝑁 and maximum peak to peak value µ 𝑉𝑝𝑝
were
computed using the following equations:
𝜎 𝑁 = √
1
𝑀
∑(𝑥𝑖 − 𝐸[𝑥])2
𝑀
𝑖=1
µ 𝑉𝑝𝑝
= 𝑚𝑎𝑥(𝑥) − 𝑚𝑖𝑛(𝑥)
where M is the number of samples taken (M=9000 in this case)
and 𝐸[𝑥] is the mean of the input signal. The results obtained
were 𝜎 𝑁 = 0.0454 µ𝑉 and µ 𝑉𝑝𝑝
= 0.35 µ𝑉. By comparing these
values to the self-noise measurements shown on the ADS1299
datasheet provided by Texas Instruments Inc. ( 𝜎 𝑁 =
0.14 µ𝑉 𝑎𝑛𝑑 µ 𝑉 𝑝𝑝
= 0.98 µ𝑉), we conclude that the acquisition
circuit design and the components chosen are appropriate.
Fig 4. Self-noise measurement on the acquisition system built.
B. Performance of the classifier
To quantify the performance of the classifier itself a number
of trials was run on two 24 year old healthy male subjects. In
each of these trials, the program indicated the user on which
blinking light he should focus. The trial’s results were then
categorized into three groups: Correct, Incorrect and Repeat.
Trials were placed in the Repeat category if the classifying
algorithm did not throw a result because no element in the score
vector S was greater than Ω 𝑔𝑟𝑜𝑢𝑝. Trials placed in the Incorrect
category threw a result that did not match the expected one,
according to the indication given to the user. Trials placed in
the Correct category threw a result that matched the indication

4. given to the user on that trial. Out of 66 trials, 59 were correctly
classified (Correct) and the remaining 7 were in the Repeat
category. Thus, 0 trials gave us an incorrect result. If we define
the algorithm’s accuracy as the ratio between Correct trials over
total number of Trials minus the number of trials in the Repeat
group, then the accuracy for this set of trials was 100%. On
various further tests in which users were free to spell any word
they wanted to accuracy was found to be over 98% in most
cases. This shows that the classifying algorithm is very robust
if the correct configuration parameters are chosen (filter attn.,
cutoff frequencies, blinking rectangle’s frequencies, threshold
levels, samples per trial, etc.). In most cases, however, reducing
trial duration (by taking less samples, for example) causes a
drop in accuracy and writing speed. Likewise, raising the
thresholds causes an increase in accuracy, but a drop in writing
speed. Low accuracy translates to slower writing because the
user spends two trials just to erase the incorrect character. It is
best to find a set of parameters that yields high accuracy and a
reasonable writing speed.
IV.CONCLUSION
A small, low cost EEG acquisition circuit was designed,
built and implemented in a BCI in order to control an on-screen
keyboard. The performance of the circuit and the software
designed was excellent, achieving high precision and writing
speed. The circuit designed can be used to measure other
bioelectrical signals, such as ECG (electrocardiology).
Improvement of the shielding of the circuit, in order to
minimize external noise and interference, and implementation
of algorithms for detection and correction of artifacts are
proposed as future works for improving the presented BCI. We
hope this work promotes local research on the fields of EEG
and BCI so that these types of devices become more easily
accessible to severely disabled people, so that they can
ultimately benefit from them and improve their quality of life.
ACKNOWLEDGEMENTS
The authors of this work would like to thank the following
people for their unconditional help and support during this
project: our families and friends, the members of the
Bioengineering Lab in the Faculty of Engineering of the Mar
del Plata National University, Alejandro Uriz, Gustavo Uicich,
Jonatan Fischer, Ariel Nieto and our directors (Gustavo
Meschino and Isabel Passoni).
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