BIOMEDICAL ENGINEERING IN CHINA
Brain–Computer Interfaces
Based on Visual Evoked
Potentials
R
ecently, electroencephalogram (EEG)-based brain–
computer interfaces (BCIs) have become a hot spot in
the study of neural engineering, rehabilitation, and brain
science. In this article, we review BCI systems based on
visual evoked potentials (VEPs). Although the performance of
this type of BCI has already been evaluated by many research
groups through a variety of laboratory demonstrations, research-
ers are still facing many difficulties in changing the demonstra-
tions to practically applicable systems. On the basis of the
literature, we describe the challenges in developing practical BCI
systems. Also, our recent work in the designs and implementa-
tions of the BCI systems based on steady-state VEPs (SSVEPs)
is described in detail. The results show that by adequately consid-
ering the problems encountered in system design, signal process-
ing, and parameter optimization, SSVEPs can provide the most
useful information about brain activities using the least number
of electrodes. At the same time, system cost could be greatly
decreased and usability could be readily improved, thus benefit-
ing the implementation of a practical BCI.
Brain–Computer Interfaces
BCIs translate human intentions into control signals to establish
a direct communication channel between the human brain and
output devices. Among a variety of methods for monitoring brain
activity, the most favorable method is noninvasive EEG. Various
brain signals have been employed in the design of EEG-based
BCIs, e.g., event-related potentials (ERPs) and event-related
EEG changes [i.e., event-related desynchronization/synchroniza-
tion (ERD/ERS)] [1]–[3]. Among the current BCIs, the system
based on VEPs has been studied for a long period since the 1970s
[4]. During the last few years, it has still received strongly
increased attention in BCI research [5]–[14]. Studies on the VEP
BCI demonstrate convincing robustness of system performance
through many laboratory and clinical tests. The recognized
advantages of this BCI include easy system configuration, little
user training, and a high information transfer rate (ITR).
Visual Evoked Potentials
VEPs reflect the visual information-processing mechanism in the
brain. According to the knowledge of brain electrophysiology,
VEPs corresponding to low stimulus rates are categorized as
transient VEP (TVEP), and those corresponding to rapidly repeti-
tive stimulations are categorized as steady-state VEP (SSVEP)
[15]. Ideally, a TVEP is a true transient response to a stimulus
when the relevant brain mechanisms are in resting states. It does
not depend on any previous trial. If the visual stimulation is
repeated with intervals shorter than the duration of a TVEP, the
responses evoked by each stimulus will overlap each other, and
an SSVEP is generated. In this circumstance, the brain is consid-
ered in a steady state of excitability. So far, both TVEP and
SSVEP have already been applied in BCI research. Because of
the characteristi cs caused by different stimuli, analysis of the
TVEP is based on temporal methods such as template matching,
whereas SSVEP detection is usually performed by frequency
analysis, e.g ., power spe ctral d ensity (PS D) estimati on.
Present-Day VEP BCIs
In this section, we present a survey of VEP-based BCI sys-
tems. To emphasize the importance of an online system
design, studies only presenting offline analysis results are not
included here. To the best of our knowledge, eight groups
have developed demonstrations during the past several deca-
des [4]–[14]. Among three of the eight groups, each developed
two different systems (see [6]–[10] and [12] for details).
Detailed classification results of our survey are listed in
Table 1. The principal design attributes include operation
modality, signal recording (signals and number of channels)
and users, visual stimulus (stimulus display and number of tar-
gets), and signal processing (information encoding and decod-
ing). The listed references represent the systems designed
with the attribute subclasses.
Operation Modality
According to the necessity of employing activity in the brain’s
normal output pathways to generate brain activity, BCIs are
divided into two classes: dependent and independent [1]. The
VEP system based on gaze detection falls into the dependent
class. The generation of the VEP depends on gaze direction
controlled by motor activity of extraocular muscles. Therefore,
this BCI is inapplicable for people with severe neuromuscular
disabilities, who may lack extraocular muscle control. Never-
theless, because of the reliability and practicability of the
BY YIJUN WANG,
XIAORONG GAO,
BO HONG, CHUAN JIA,
AND SHANGKAI GAO
© WIKIPEDIA
Digital Object Identifier 10.1109/MEMB.2008.923958
Feasibility of Practical System Designs
64 IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 0739-5175/08/$25.00©2008IEEE SEPTEMBER/OCTOBER 2008
system, all eight groups have designed similar systems
employing gaze detection as the basic principle [4]–[11], [13],
[14]. In addition to amplitude modulation by gaze control,
recent neuroscience studies on visual attention also reveal that
the VEP can also be modulated by spatial attention and fea-
ture-based attention independent of neuromuscular function
[16]–[18]. These findings make it possible to implement an
independent BCI based on attentional modulation of VEP
amplitude. Up to now, one research group developed an inde-
pendent VEP-based BCI called the visual-spatial attention
control (V-SAC) BCI employing visual spatial attention to
self-regulate amplitude of SSVEPs elicited by two flicker
stimuli on the left and right sides of a screen [12]. Compared
with the dependent type, this attention-based BCI needs more
subject training and has a much lower ITR. In real-life appli-
cation, the dependent type is more practical for most users.
Thus, the dependent VEP-based BCI is more attractive than
the independent type in current BCI research.
Signal Recording and Users
Data recording in these systems differs in signals and electro-
des. Both EEG and electrocorticogram (ECoG) have been
used in VEP-based BCIs. In the brain response interface (BRI)
system, an epidural electrode strip implanted over the visual
cortex was used in an amyotrophic lateral sclerosis (ALS)
patient [5]. All the other systems use noninvasive scalp EEG
electrodes. When compared with ECoG, although the EEG
signal has a low signal-to-noise ratio (SNR), it is still more
acceptable to the users because of its noninvasiveness. The
number of electrodes can be categorized as two electrodes and
multiple electrodes (4–12 electrodes). Although multichannel
data are more reliable for data analysis, considering the prac-
ticability, most systems are designed with few electrodes. The
O1 and O2 electrode positions of the international 10–20 sys-
tem are widely used. The bipolar electrode placement has been
applied in some systems to obtain a cleaner signal by cancel-
ing common background activities [6], [11].
Most systems are in the stage of laboratory demonstration
with healthy volunteers as the subjects. Toward clinical appli-
cation, two systems have been tested with patients. The BRI
system was successfully applied to help an ALS patient to
operate a word-processing program [5]. Also, in our previous
study, the real-life application of an SSVEP-based BCI was
investigated on patients with spinal cord injury (SCI). The rea-
son for performance decrease from the laboratory test to the
clinical test was discussed in [9]. In the future, much more
work is required to make the system more robust in daily-life
applications for the motion disabled.
Table 1. Classification of online VEP-based BCI systems.
Design Attribute Attribute Subclass
References with
Attribute Subclass
Group
Counts
Operation modality Dependent [4]–[11], [13], [14] 8
Independent [12] 1
Signals EEG [5]–[14] 7
ECoG [4] 1
Number of channels 2 (e.g., O1 and O2) [6]–[10], [12], [14] 4
Multiple [4], [5], [11], [13] 4
Users Healthy volunteers [4], [6]–[8], [10]–[14] 8
Patients: ALS [5] 1
SCI [9] 1
Stimulus display LCD/CRT [5]–[7], [9], [10], [12]–[14] 6
Flashtube/LED [4], [8], [11] 3
Number of targets Single [4], [6] 2
Multiple (2–64) [5], [7]–[14] 7
Information coding TVEP [4] 1
SSVEP: tSSVEP [5], [14] 2
fSSVEP [6]–[13] 5
Information decoding Temporal waveform [4] 1
Cross-correlation [5] 1
Phase coherent detection [14] 1
PSD analysis [6]–[13] 5
Even for the groups that have realized assistive
device control, many of them remain at the
demo stage in laboratories.
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE SEPTEMBER/OCTOBER 2008 65
Visual Stimulator
The visual stimulator commonly consists of flickering targets
in the form of alternating colors or a reversing checkerboard. In
the VEP-based BCIs, the CRT/LCD monitor or flashtube/light-
emitting diode (LED) is used for stimulus display. Among all
the groups, six groups use a monitor to display visual stimula-
tion, whereas three use a flashtube or LEDs. A computer moni-
tor is convenient for target alignment and feedback presentation
through programming, but for a frequency-coded system, the
number of targets is limited because of the refresh rate of a
monitor. Therefore, the LED stimulator is preferable for a
multiple target system. The flickering frequency of each LED
can be controlled independently by a programmable logic
device. Using such a stimulator, a 48-target BCI was reported
in [8]. In addition, the LED stimulator is more suitable for com-
posing a portable system that does not depend on a computer. A
wearable stimulator can significantly improve the practicability
of VEP-based BCIs.
Among all the systems, the number of stimulation targets
varies between one and 64, which leads to a range of system
performance. Generally, a system with more targets can
achieve a higher ITR. For example, in tests of the 13-target
and two-target systems, the subjects had an average ITR of
43 and 10 b/min, respectively [9], [10]. However, because a
stimulator with more targets is also more exhausting for users,
the number of targets should be considered according to a
tradeoff between system performance and user comfort. In
addition, the optimal number of stimuli also depends on the
usable bandwidth of SSVEPs, which is subject specific [9].
The extended use of the frequencies in which the SSVEPs
have low SNRs will not increase but decrease the ITR.
Signal Processing: Information Coding and Decoding
According to the VEP signals used for information coding, cur-
rent VEP BCIs fall into two groups: TVEP and SSVEP. The first
group uses TVEP to detect gaze direction. Spatial distributions
of TVEPs elicited by a stimulus located in different visual fields
were used to identify visual fixation by Vidal in the 1970s [4].
The SSVEP is used by the other group as the communication
medium. According to the approach for information coding, the
SSVEP-based BCIs can be further divided into subgroups of
temporal coding and frequency coding. To facilitate presenta-
tion, we refer to them as tSSVEP and fSSVEP, respectively. The
BRI system designed by Sutter is a tSSVEP-based BCI [5]. The
occurrencetimeofasequenceofVEPswasusedtolabelastim-
ulation target. In other words, different targets evoke tSSVEPs
identical in shape of waveform but different in time of occur-
rence. Another tSSVEP BCI is the phase-coded system designed
by Kluge et al. with two stimuli at a single frequency but differ-
ent phases to elicit tSSVEPs [14]. The other SSVEP BCIs utilize
the fSSVEP to encode information. One or more frequency-
coded stimulation targets are used to elicit fSSVEPs. During sys-
tem operation, the amplitude of the fSSVEP is regulated by gaze
or spatial attention.
The approaches for information decoding depend on the
protocol of coding. Feature extraction of the TVEP is based on
waveform detection in the temporal domain [4]. Similarly, a
template-matching approach by cross-correlation analysis was
used to detect the tSSVEP in the BRI system [5]. In the phase-
coded SSVEP system, an approach using phase-coherent
detection between the stimulation and SSVEP has been pro-
posed [14]. In the fSSVEP-based BCIs, PSD analysis is most
widely used [6]–[13]. After PSD estimation, the frequency
components at the fundamental and harmonic frequencies are
commonly used as features for classification. It must be men-
tioned that the analysis of the TVEP and tSSVEP needs accu-
rate time triggers from the stimulator, whereas they can be
omitted in power-based detection of the SSVEP.
Practical System Designs
With the VEP-based BCIs discussed in this article, many stud-
ies have been performed to implement and evaluate demon-
stration systems in laboratory settings; however, the challenge
facing the development of practical BCI systems for real-life
applications needs to be emphasized. According to the survey
made by Mason et al., existing BCI systems could be divided
into three classes: transducers, demo systems, and assistive
devices [19]. Among the 79 BCI groups investigated, ten have
realized assistive device (13%), 26 have designed demonstra-
tion systems (33%), and the remaining 43 are only in the stage
of offline data analysis (54%). Even for the groups that have
realized assistive device control, many of them remain at the
demo stage in laboratories. In other words, there is still a long
way to go before BCI systems can be put into practical use.
However, as an emerging engineering research area, if they
remain in the laboratory for scientific exploration, their influ-
ence on human society will certainly be limited. Thus, the
feasibility for practical application is a serious challenge in
the study of BCI systems. A practical BCI system must fully
consider the user’s human nature, which includes the follow-
ing aspects.
Noninvasive to Users
An important argument of BCI users is that time is not an issue
for completely paralyzed patients. In Birbaumer’s research,
only one of 17 ALS patients agreed to use subdural electrodes,
despite the fact that implanted electrodes can significantly
improve system performance when compared with scalp elec-
trodes [20]. So far, noninvasive EEG-based BCI systems are
the first choice for the majority of users, including severely
paralyzed patients. Thus, extracting useful information from
the scalp EEG stably and reliably is the accompanying
Reducing the number of electrodes in BCI
systems is a critical issue for the successful
development of clinical applications
of BCI technology.
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE SEPTEMBER/OCTOBER 200866
problem, which depends on data-recording configurations and
information-processing techniques.
Convenient and Comfortable to Use
Current EEG systems use standard wet electrodes, in which
electrolytic gel is required to reduce electrode-skin interface
impedance. Using electrolytic gel is uncomfortable and incon-
venient, especially if a large number of electrodes are adopted.
First, preparation of EEG recording before BCI operation
takes a long time. Second, problems caused by electrode dam-
age or bad electrode contact can occur. Third, an electrode cap
with a large number of electrodes is uncomfortable for users
to wear and thus unsuitable for long-term recording. More-
over, an EEG recording system with a large amount of chan-
nels is usually quite expensive and nonportable. For all these
reasons, reducing the number of electrodes in BCI systems is a
critical issue for the successful development of clinical appli-
cations of BCI technology.
Stable and Reliable System Performance
Compared with the environment in an EEG laboratory, electro-
magnetic interference and other artifacts [e.g., electromyogram
(EMG) and electrooculogram (EOG)] are much stronger in
daily home life. Suitable measures should then be applied to
ensure the quality of the recorded EEG. Considering data
recording in unshielded environments, the use of an active
electrode may be much better than a passive electrode. It can
ensure that the recorded signal is insensitive to interference
[21]. To remove the artifacts in EEG signals, additional record-
ings of EMG and EOG may be necessary, and advanced tech-
niques for online artifact cancelling should be applied.
Moreover, to reduce the dependence on technical assistance
during system operation, ad hoc functions should be provided
in the system to adapt to the individual diversity of the user and
nonstationarity of the signal caused by changes in the electrode
impendence or brain state. These functions must be convenient
to use. For example, software should be able to detect bad
electrode contact in real time and automatically adjust algo-
rithms to be suitable for the remaining good channels.
Low-Cost Hardware
Although BCI is known as a high-tech application, it should
be remembered that its direct users are mostly disabled per-
sons. The system cannot be popularized if it costs too much,
no matter how good its performance is. To reduce the cost,
two main aspects should be considered. The first is the EEG
recording equipment. It need not satisfy the requirements of a
commercial EEG system but only those of the specific BCI
system. The cost lies with the parameters such as the number
of channels, the bandwidth, and the sampling rate. The second
is the signal processing unit. For most current systems, signal
processing is executed on a computer. To eliminate the cost of
a computer as well as design a portable system, a digital signal
processor (DSP) can be employed to construct a system not
dependent on a computer.
The requirements for a BCI system to reach the stage of practi-
cal application have been listed earlier. Some of the requirements
complement each other, e.g., reducing the electrode number can
not only enhance the convenience of system operation but also
help to decrease the system cost. Other requirements may be
contradictory to each other, e.g., although using scalp EEG for
signal acquisition is noninvasive, it will make signal processing
more difficult because of the lower SNR. The key point of the
research is to weigh the importance of various factors and over-
come the bottleneck, so as to find a comprehensive solution for
the problems. Although solving all of these problems within a
short time seems difficult, our recent research looks promising
for providing a comprehensive solution. The SSVEP-
bas ed BC I sys te m designed by us is introduced in the next
section to show its practical implementation.
Practical BCI System Based on SSVEP
By gazing at different visual targets bearing different features
and located at different positions, a specific target can be selected
based on its specific feature and then used to control the corre-
sponding peripheral device. This is the basic principle of our
SSVEP-based BCI. At present, it is one of the BCI systems that
are most promising for practical use. Toward the aim of practical
applications, great efforts have been made by us in facilitating
system configuration and improving system performance.
According to the practicality issues aforementioned, we focus on
the two aspects of lead selection and information processing,
which can significantly reduce system cost and improve system
performance. In our system, only one bipolar lead on subject-
specific positions is needed to obtain the SSVEP with a high
SNR. The advantages of this simple electrode layout include
low-cost hardware, convenient operation, and easy calculation,
which can benefit the implementation of a practical BCI. Appro-
priate approaches to information processing can ensure a more
stable system performance. Frequency features of SSVEP har-
monics and phase-locking detection between the SSVEP and the
visual stimulation have been investigated in our designs of
frequency-coded and phase-coded systems.
In this section, both types (frequency-coded and phase-
coded) of SSVEP-based BCI designed by us are described. In
tests of the system based on frequency features (dialing a tele-
phone number), with optimized system parameters for five
participants, an average ITR of 46.68 b/min was achieved
[22]. For the study of phase coding, a preliminary demonstra-
tion of the system has been designed and tested. The system
can reliably classify SSVEP responses for six phase-coded
stimuli flickering at the same frequency. For one subject, an
The recognized advantages of VEP-based BCIs
include easy system configuration, little user
training, and a high information transfer rate.
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE SEPTEMBER/OCTOBER 2008 67
accuracy of 86.7% was achieved, demonstrating the practic-
ability of phase coding in an SSVEP-based BCI.
SSVEP System Based on Frequency Feature
Frequency coding and PSD analysis is the method employed
by most SSVEP-based BCI systems. Figure 1 shows its basic
diagram. Facing a number of visual spots flickering at differ-
ent frequencies, the subject gazes at one of them, generating
an SSVEP with specific frequency components. Figure 1(c) is
the amplitude spectrum of the SSVEP evoked by the visual
spot flickering at 7 Hz. Spectrum peaks appear at 7 Hz, 14 Hz
(second harmonic), and 21 Hz (third harmonic). When the
peaks are detected, the flickering spot the subject is watching
can be identified, and the corresponding command can then be
executed.
The SSVEP BCI based on frequency coding seems to be
rather simple in principle, but a number of problems (such as
selection of electrodes and stimulating frequencies, algorithm
of feature extraction, and threshold setting) have to be solved
during its implementation. Among them, lead position selec-
tion and frequency feature extraction are the most important
[7]–[9]. As mentioned previously, because of the difference in
the subject’s physiological conditions, a preliminary experi-
ment should be carried out for a new user to set the subject-
specific optimal parameters.
Lead Position
To get SSVEPs with a high SNR using the least number of elec-
trodes is the goal of lead selection. Only one bipolar lead is
chosen as the input in our system. From physiological knowl-
edge, VEP can be recorded with maximum amplitude at the occi-
pital region. So, the electrode giving the strongest SSVEP, which
is generally located in the occipital region, is selected as the sig-
nal channel. The location of the reference channel is searched
under the following considerations: the amplitude of the SSVEP
in it should be lower, and its position should lie in the vicinity of
the signal channel so that the noisecomponentinitissimilarto
that in the signal channel. A high SNR can then be gained when
the potentials of the two electrodes are subtracted. Figure 2
shows an example of a significant enhancement of SSVEP SNR
derived from this lead selection method. Most spontaneous back-
ground activities are eliminated after the subtraction, whereas
the SSVEP component is retained. Details of the method can be
found in [9]. According to our experience, although the selection
varies between subjects, once it
is selected, it is relatively stable
with respect to time. This find-
ing makes the lead selection
method feasible for practical
BCI application. For a new sub-
ject, the multichannel recording
needs to be done only once for
optimization of lead position.
Frequency Feature
Because of the nonlinearity
during information transfer in
visual system, strong harmon-
ics may often be found in the
SSVEPs. Muller-Putz et al.
investigated the impact of
using SSVEP harmonics on the
classification result of a four-
class SSVEP-based BCI [11].
In their study, the accuracy
obtained with combined har-
monics (up to the third har-
monic) was significantly higher
than with only the first har-
monic. In our experience, for
some subjects, the intensity of
thesecondharmonicmaysome-
times be even stronger than that
of the fundamental component
(Figure 1). Thus analysis of the
frequency band should cover
the second harmonic and the
frequency feature has to be
taken as the weighted sum of
their powers:
P
i
¼ aP
f
1i
þ (1 a) P
f
2i
,
i ¼ 1, 2, ...N, (1)
10
POz
–10
0 0.2 0.4 0.6 0.8 1
0
10
PO2
–10
0 0.2 0.4 0.6 0.8 1
0
5
PO2-POz
–5
0 0.2 0.4
Time (s)
Amplitude (uV)
0.6 0.8 1
0
0.04
POz
0
5 101520253035
5 101520253035
5 101520253035
0.02
0.04
PO2
0
0.02
0.15
PO2-POz
0
Frequency (Hz)
Normalized Power Spectral Density
0.1
0.05
Fig. 2. Monopolar and bipolar waveforms and normalized PSDs (sum of the spectrum is nor-
malized to one) of EEG signals on two optimal electrodes (PO2 as the signal electrode and
POz as the reference electrode) for one subject. The stimulation frequency is 13 Hz.
3.5
3
2.5
7 Hz
14 Hz
21 Hz
2
Amplitude (µV)
1.5
1
0.5
0
51015
Frequency (Hz)
(c)
20 25
(b)(a)
Fig. 1. SSVEP BCI based on frequency coding. (a) The visual stimulator consists of targets
flickering at different frequencies. (c) The SSVEP elicited by the 7-Hz stimulation shows
characteristic frequency components with peaks at the fundamental and harmonic fre-
quencies at the O2 electrode.
IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE SEPTEMBER/OCTOBER 200868
