High Frequency Steady-State Visual Evoked Potentials:

An Empirical Study on Re-test Stability for Brain-Computer

Interface Usage

Jan Ehlers

1 a

, Thorsten Lueth

and Axel Graeser

Institute of Media Informatics, Bauhaus University Weimar, Bauhausstr 11, 99423 Weimar, Germany

Institute of Automation, University of Bremen, Otto-Hahn-Alle 1, 28359 Bremen, Germany

Keywords: Brain-Computer Interfaces, SSVEP, EEG.

Abstract: Steady-State Visual Evoked Potentials (SSVEP) constitute an established approach to operate a Brain-

Computer Interface (BCI). In contrast to stimulation between 13 and 17 Hz, stimulation above 30 Hz is

considered less annoying and diminishes the risk of epileptic seizures. However, high-frequency BCIs usually

feature slow processing speed and accuracy rates which reduces user satisfaction. We investigate the re-test

stability of resonance frequencies between 30 and 50 Hz in 18 participants over a period of 40 days, including

seven consecutive runs. Aim is to determine individual resonance profiles for recurring BCI usage that make

time-consuming calibration phases no longer necessary. Preliminary findings of a clinical sample are reported

as well. Results indicate that seven of nine frequencies fail to repeatedly induce stable responses. However,

stimulation with 32 and 40 Hz induced strong and recurring SSVEP in the vast majorities of trials.

Consequently, high-frequency based BCI usage will continue to presuppose individual calibration. Apart from

this, since 40 Hz oscillations are suggested to play a key role in various brain functions, it is reasonable to

assume pronounced cortical reactions to 32 Hz to also constitute a neuronal oscillator that is functional active

during cognitive processing.

1 INTRODUCTION

Intermittent photic stimulation (IPS) of frequencies at

a rate of 4 Hz or higher evokes a synchronized cortical

response of rhythmic activity linked to the triggering

frequency (Herrmann, 2001). Oscillatory EEG

activity that arises from repetitive stimulation is

referred to as Steady-State Visual Evoked Potentials

(SSVEP) and constitutes an important clinical test to

detect photoparoxysmal responses. Recorded

primarily over early visual processing areas of the

brain, it is assumed to occur due to neuronal

oscillators that selectively respond to predetermined

frequencies, so-called resonance frequencies (Makeig

et al., 2002). Amplitudes of SSVEP activity seem to

peak at 15 Hz (Pastor et al., 2003) but are also

reported to be correlated with the EEG alpha-range

(8-12 Hz), indicating strongest responses near a

dominant resting frequency (Jin et al., 2000; Ehlers et

al., 2012). A previous study (Herrmann, 2001)

demonstrates the origin of SSVEP activity up to 100

https://orcid.org/0000-0002-4475-2349

Hz and reports pronounced cortical reactions to

flickering stimuli in the 10, 20, 40 and 80 Hz range

compared to adjacent frequencies.

In the recent past, SSVEP activity has been

applied successfully to operate a Brain-Computer

Interface (BCI) (Stawicki et al., 2016; Chabuda et al.,

2018). A BCI is a non-muscular communication

system that classifies EEG activity patterns and

translates them in real time into commands for

various applications. As indicated above, SSVEP-

based BCIs require overt attentional shifts between

constant flickering sources whereas each stimulation

frequency is associated with a certain command.

Usually, SSVEP frameworks apply stimulation

between 13 and 17 Hz since this range is known to

produce prominent and easy to detect SSVEP

(Allison et al., 2010; Ehlers et al., 2012; Stawicki et

al., 2016). However, visual annoyance and

photosensitivity pose a problem, especially in this

particular spectrum. As a consequence, recent

research focuses IPS above 30 Hz. Higher frequency

164

Ehlers, J., Lueth, T. and Graeser, A.

High Frequency Steady-State Visual Evoked Potentials: An Empirical Study on Re-test Stability for Brain-Computer Interface Usage.

DOI: 10.5220/0008348401640170

In Proceedings of the 3rd International Conference on Computer-Human Interaction Research and Applications (CHIRA 2019), pages 164-170

ISBN: 978-989-758-376-6

stimulation has proven to reduce the risk of epileptic

seizures and is usually considered less annoying

during long-term usage (Molina, 2009; Müller et al.,

2015; Chabuda et al., 2018). Due to poor signal-to-

noise ratios (SNR), however, processing speed of a

high frequency SSVEP framework is comparably

slow and associated with considerably lower

accuracy rates (Molina, 2009; Ehlers et al., 2012).

Similar to SSVEP activity during low frequency

stimulation, induced cortical reactions between 30

and 46 Hz seem to occur selectively and feature inter-

individual differences (Ehlers et al., 2012; Stawicki et

al., 2016; Chabuda et al., 2018). Current SSVEP-

based frameworks above 30Hz (e.g. Chabuda et al.,

2018) apply time-consuming calibration phases prior

to BCI usage to detect prominent resonance

frequencies. However, it’s not clear whether a once-

identified individual frequency set will provide the

same resonance performance during repeated usage.

To our knowledge, the re-test stability of resonance

frequencies above 30 Hz over a longer period has not

been investigated yet. Repeated stimulation carried

out over several test days should enable to detect

temporally stable oscillators that produce distinct and

recurring SSVEP and make time-consuming

calibration no longer necessary. For this purpose, the

current study applies four consecutive sessions with

varying time intervals in-between. In controlled

settings, nine stimulation frequencies between 30 and

46 Hz are inspected for SNRs and re-test stability

over a period of 40 days. Furthermore, initial results

with regard to a clinical pilot study are provided to

give an impression of cortical reactions to flickering

stimuli in potential target users.

2 METHODS

2.1 Design and Procedure

To test the stability of induced driving responses,

SSVEP screenings were arranged in four sessions,

including two runs of nine randomized trials in two

LED-on phases each (figure 1). Intervals between

sessions were controlled as follows: 1st session / 2nd

session: 1st session + 2 days / 3rd session: 2nd session

+ 1 week / 4th session: 3rd session + 1 month. Due to

the availability of our participants, daytimes could not

be controlled and varied between 10:00 am and 16:00

pm. Lab environment exhibited a common level of

background noise, lighting conditions were kept

constant at 780 lux.

Participants were seated in a comfortable chair,

40 cm in front of an LED array. LEDs had an edge

length of 20x14 mm and were marked in ascending

order with numbers from 1 to 4; vertical angle of

vision was 0.3°. The experimenter indicated a

specific number before start and participants were

instructed to focus the given LED during a complete

trial of 8,75 seconds. Nine frequencies between 30

and 46 Hz (in a step of 2 Hz: 30, 32, 34, 36, 38, 40,

42, 44, 46) were assigned in randomized order at

varying positions on the array. Since IPS of just under

four seconds is sufficient to selectively induce

resonance properties (Molina, 2009), each frequency

was tested in two successive LED-on segments of

3.75 seconds with an LED-off segment of 1.25

seconds in between. After a five-minute rest,

screening procedure was repeated. The fourth session

consisted of only one run. Accordingly, each

volunteer participated in seven screenings. Flickering

frequencies were controlled by a microcontroller

(PIC16F877, Microchip, Chandler, Arizona, USA).

Figure 1: Overall testing procedure.

Additionally, a pilot study on high frequency

SSVEP activity in potential BCI target users was

carried out in a usual working area. Moreover, except

for stimulation frequencies and trial duration,

experimental setup and testing equipment differed

strongly from the current specifications. Frequencies

were randomly assigned to four square shaped LEDs

(edge length: 7x7 cm) arranged around a computer

monitor. Viewing distance and environmental

conditions could not be controlled. Also, due to state

of health, patients performed only a single session

including two runs. For more details on the SSVEP

framework see (Ware et al., 2010).

2.2 Participants

18 volunteers (16 females; mean age: 24 years, SD:

4) were included in the current study. Participants had

normal or corrected-to-normal vision and no prior

experiences with BCIs. They reported no history of

head injury and no neurological or psychiatric

disorder. Participants were informed that repetitive

stimulation might lead to epileptic seizures and

High Frequency Steady-State Visual Evoked Potentials: An Empirical Study on Re-test Stability for Brain-Computer Interface Usage

165

confirmed that they never suffered from epilepsy or

any photosensitive reactions. Information on regular

medication was not collected. Participants received

course credits, written informed consent was obtained

prior to the start.

The clinical sample consisted of 16 potential BCI

target users (three females; mean age 40 years, SD:

11) that were tested on basis of a differing SSVEP

setup. These volunteers were recruited from the

Cedar Foundation (Belfast, Northern Ireland), a non-

profit organization that supports people with various

disabilities. The sample included patients that suffer

from severe handicaps due to brain/spine injuries,

stroke or cerebral palsy.

All measurements were performed in accordance

with the Declaration of Helsinki and approved by the

ethical board of the associated EU-project “BRAIN”

(No. 224156).

2.3 Data Collection

EEG data was recorded from the surface of the scalp

via six water-based electrodes (Twente Medical

System International (TMSI), Oldenzaal,

Netherlands). Electrodes were mounted according to

the extended 10-20 system of electrode placement

[19] at PZ, PO3, PO4, O1, OZ, O2, O9, O10 and

grounded at AFZ. Shielded cables connected

electrodes and the high-impedance amplifier system

(Porti32, TMSI). Sampling frequency was set to 2048

Hz with a high-pass filter at 0.1 Hz. BCI2000

software (Schalk et al., 2004) was applied for data

acquisition and storage. The signal processing

module was implemented in the BCI2000 framework.

2.4 Signal Processing

During stimulation with a specific frequency, the

power of all (eight) others is estimated

simultaneously. Successful stimulation will induce a

considerable power increase within the associated

frequency. Assuming stimulation with a flickering

frequency of f Hz, SSVEP activity measured at

electrode number i can be estimated as:

  

 

TSttbkftay

kikii







0,2sin



(1)

where b(t) describes the noise, TS the time segment

and N

the number of harmonics (Friman et al., 2007).

Each sinusoid on each electrode has its own

amplitude and phase. The nuisance signals b(t) can

have several origins, e.g. concurrent brain activity,

breathing artefacts or environmental disturbances. To

improve target frequency detection, nuisance signals

have to be decreased and the envisaged SSVEP signal

to be magnified. This is achieved by a linear

combination of signals determined by the N

electrodes into new channels s (Mandel et al., 2009).

With N

as the number of channels, a single channel

is defined by:

   

ilil

Nltywts







(2)

Weighting factors w

i,l

of the spatial filtering are

determined on basis of the Minimum Energy

Combination (MEC) that has proven good

performance in former applications (Allison et al.,

2010; Volosyak et al., 2010). The MEC allows the

combination of an arbitrary number of electrodes.

The combination matrix is constantly adapted in real

time to react to electrodes that may lose contact or

transmit poor signals. These electrodes receive a low

weighting or might even be ignored to provide a

proper signal quality over time. A sliding window of

two seconds ensures sufficient EEG data for the

analysis.

Total power of the SSVEP frequency is estimated

slightly different to the squared Discrete Fourier

Transform (DFT) magnitude (Friman et al., 2007;

Mandel et al., 2009). With X

as the SSVEP model

containing the sine and cosine pairs with the

harmonic frequencies, the power in the kth SSVEP

harmonic frequency in the lth channel signal s

, is

estimated to:

(3)

Last step of signal processing is the normalization

of the absolute SSVEP activity for each stimulation

frequency (which is the average of the power over all

spatially filtered components and all N

SSVEP

harmonic frequencies) into relative values in order to

yield comparability (Volosyak et al., 2010):





(4)





(5)

SNR is calculated for each frequency f. The

normalized and averaged SSVEP signal of a

frequency during stimulation is divided by the noise

signal. Here, the normalized (4) and averaged signal

of a target frequency obtained during the LED-off

phase is considered as noise.

offf

onf

SNR



(6)

klk

sXP 

CHIRA 2019 - 3rd International Conference on Computer-Human Interaction Research and Applications

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The higher the SNR for a frequency, the higher

the difference between SSVEP activity during the

LED-on phase compared to the LED-off phase.

2.5 Statistical Analysis

A Kolmogorov-Smirnov test revealed that the data is

not well modeled by a normal distribution. We

applied an analysis of variance by ranks for

dependent measures according to Friedman to

compute differences of SNRs between all

frequencies. During signed-rank tests, alpha level

accumulates and Bonferroni method was applied for

correction (adjusted alpha level: 0.0014). To

determine effect sizes, we used the Pearson

correlation (r) on basis of z-values of the Wilcoxon

tests (r= z/square root(n)), (n= 126 observations).

3 RESULTS

The non-parametric Friedman test revealed

considerable differences among SNRs elicited by all

nine stimulation frequencies (χ

=434,76; p< .001).

Averaged across sessions, IPS on basis of 32 and 40

Hz induced significant higher SNRs compared to all

other frequencies (p< .001). Also, IPS with 32 Hz

elicited stronger SSVEP compared to stimulation

with 40 Hz (z= -4,40; p< .001) (figure 2). Differences

between 32 Hz and all other frequencies (except 40

Hz) amount to r=0.86, indicating large effects with

regard to Cohen’s benchmark. Between 40 Hz and

adjacent frequencies (except 32 Hz), effect sizes

amount to r=0.83.

Figure 2: Averaged signal-to-noise ratios across all IPS

trials.

In 92% of all cases (four LED-on periods per

session for each frequency), 32 Hz proved to induce

strongest or at least second strongest cortical

reactions. 40 Hz turned out to be the dominant or

second dominant resonance frequency during 68% of

all cases. The remaining frequencies only

sporadically provoked stronger responses compared

to all others. Stable SSVEPs beyond 32 and 40 HZ

could not be observed across the testing sessions

Figure 3 depicts the SSVEP power across time for

IPS with 32 and 40 Hz as well as for two adjacent

frequencies (30 and 42 Hz). For reasons of clarity,

results of only ten participants are depicted (averaged

across all sessions). Power was calculated

simultaneously for all nine frequencies, proportional

values of the four given frequencies are illustrated for

LED-on segments (3.75 seconds, data points 0 to 30

and 40 to 70) and the in-between LED-off segment

(1.25 seconds, data points 30 to 40). The spatial filter

combines all signals of the electrode placement.

On average, IPS on basis of 32 and 40 Hz evokes

a distinct physiological response. SSVEP activity

occurs approx. one second after stimulus-onset as a

linear increase in power. Peak amplitudes are

observed after approx. 2.5 seconds. Subsequent to

stimulus-offset, SSVEP power rapidly declines and

falls back to pre-stimulus level. Relative power of a

stimulation frequency may theoretically amount to

100%, given that power of all others is zero.

Assuming all nine frequencies to contribute the same

would result in 11.11% each. During IPS with 32 Hz,

its specific share increases to approx. 65% of the

overall activity; IPS on basis of 40 Hz reaches approx.

40% of the total power. In contrast, stimulation with

adjacent frequencies (here: 30 and 42 Hz) induces no

considerable SSVEP; on average, their respective

share in the overall signal remains the same as during

LED-off phases.

Figure 3: SSVEP power characteristics during IPS with 30,

32, 40 and 42 Hz. Individual averages of ten participants

across seven runs. Abscissa: data points.

Due to differences in methods, procedure and

equipment, findings from the clinical pilot study are

not directly comparable to the above-mentioned

results. However, cortical reactions to IPS with 32

and 40 Hz are still depicted to provide an impression

on their resonance properties also in neurological

patients and less controlled environments (figure 4).

High Frequency Steady-State Visual Evoked Potentials: An Empirical Study on Re-test Stability for Brain-Computer Interface Usage

167

Averaged across all four LED-on periods, we observe

a linear increase in 32 Hz power for over three

quarters of all participants; again, SSVEP activity

during stimulation with 40 Hz occurs comparably less

pronounced. Latencies to stimulus-onset are similar

to the sample of heathy participants. However, due to

the lack of comparability and the low amount of data,

we refrain from general statements or inferential

statistical analysis.

Figure 4: SSVEP power characteristics during IPS with 32

and 40 Hz for the clinical sample. Individual averages of

ten participants across two runs. Abscissa: data points.

4 CONCLUSIONS

IPS above 30 Hz largely diminishes the risk of

photosensitivity and reduces visual annoyance.

However, BCIs that apply high frequency stimulation

usually lack adequate accuracy rates and processing

speed due to low signal-to-noise ratios and random

subsets of frequencies (Ehlers et al., 2012). In a recent

study, Chabuda et al (2018) utilize IPS between 30

and 39 Hz and observe strong SSVEP in eight out of

ten frequencies for more than half of their

participants. However, no specific resonance

frequency emerged particularly common, indicating

high interindividual differences with regard to

cortical resonance above 30 Hz. Presupposing this, it

would be necessary to apply time-consuming

calibration phases for each individual prior to BCI

use. Also, it’s not clear whether a once-identified

individual frequency set will provide the same

resonance performance during repeated usage. The

current study aims to determine resonance

frequencies above 30 Hz that produce stable and

recurring SSVEP. Similar to findings during IPS

featuring low (Jin et al., 2000; Ehlers et al., 2012) and

high frequency stimulation (Chabuda et al., 2018), we

assume cortical responses to occur selectively and to

exhibit interindividual differences.

The current findings indicate that except for 32

and 40 Hz, none of the considered stimulation

frequencies (30, 34, 36, 38, 42, 44, 46 Hz) repeatedly

induce stable cortical responses. Though all of them

occasionally produce strong SSVEP compared to

adjacent frequencies, individual resonance profiles

for regular BCI usage could not be defined. IPS on

basis of 32 and 40 Hz, however, induced pronounced

and recurring SSVEP in all participants and in the

vast majorities of trials. Also, although not validated

yet, results seem to be transferable to participants

featuring various neurological diseases. For these

users it is of particular importance to be equipped

with reliable and high-performing systems. Further

research on clinical users need to be carried out to

evaluate whether the effects prevail in longer-term

studies and may ensure adequate usage in future

scenarios, for example in the area of smart homes or

rehabilitation robotics.

Considering neurophysiological research over the

recent past, strong reactions to flickering stimuli of 40

Hz are hardly surprising. 40 Hz oscillations are

assumed to play a significant role in cognitive

functions, including (but not limited to) visual feature

binding (Busch et al., 2004; Basar et al., 2016) or

attention processing (Herrmann et al., 1999). The

disposition to external stimulation could therefore be

considered as an indication that a particular frequency

plays a decisive role in cognitive processing. Similar

correlations have been reported for oscillations near a

dominant resonance frequency in the alpha range

(Pastor et al., 2003; Ehlers et al., 2012). Given that

IPS identifies neuronal oscillators, synchronized

responses to stimulation with 32 Hz may also suggest

functional relevance of this particular frequency.

While adjacent frequencies display no or only few

distinct physiological responses, 32 Hz exhibits even

stronger resonance properties compared to

stimulation with 40 Hz. Re-test stability across

various sessions at different times of day suggests that

factors like vigilance, biorhythm or any kind of

psychological state have little or no effect on the

resonance properties. However, at this point, we

cannot make any assumptions of a certain role of 32

Hz oscillations in cognitive processing.

High frequency SSVEP-based BCIs of the recent

past suggest a four- or five-way command interface,

allocating each stimulation frequency to a different

command, e.g. “up”, “down”, “left”, “right” and

“select” for screen-based spelling applications

(Ehlers et al., 2012; Chabuda et al., 2018). As

indicated above, these systems entail considerably

lower accuracy rates/information transfer rates

compared to BCIs that apply the same number of

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stimulation frequencies (or even more) in the range

between 13 and 17 Hz (Allison et al., 2010; Ehlers et

al., 2012; Stawicki et al., 2018). Individual frequency

sets that may improve processing accuracy of high

frequency SSVEP BCIs could not be established

during this study. However, it is to be noted that we

excluded numerous frequencies from our screening

(31, 33, 35, 37, 39, 41, 43, 45 Hz) due to the overall

duration of a session. Considering the selectivity of

cortical responses to IPS, it cannot be ruled out to

identify further resonance frequencies above 30 Hz.

Due to only two stable and recurring resonance

frequencies so far (32 & 40 Hz), high frequency based

BCI usage will continue to presuppose individual

calibration beforehand. However, for multimodal

interaction concepts that include various

physiological input options (e.g. eye movements), the

application of 32 and 40 Hz stimulation may provide

a further promising communication channel.

ACKNOWLEDGEMENTS

The current research has received funding from the

European Community's Seventh Framework

Programme under grant agreement No 224156. The

authors express their gratitude to all volunteers,

especially the tenants of the Cedar Foundation. We

sincerely thank Melanie Ware and Alexander

McRoberts from the University of Ulster for the

smooth cooperation during the patient testing.

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