City University of New York (CUNY) City University of New York (CUNY)

CUNY Academic Works CUNY Academic Works

Dissertations and Theses City College of New York

2013

A novel visual stimulation paradigm: exploiting individual primary A novel visual stimulation paradigm: exploiting individual primary

visual cortex geometry to boost steady state visual evoked visual cortex geometry to boost steady state visual evoked

potentials (SSVEP) potentials (SSVEP)

MARTA ISABEL VANEGAS ARROYAVE CUNY City College

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A novel visual stimulation paradigm: exploiting individual primary visual cortex geometry to

boost steady state visual evoked potentials (SSVEP)

Thesis

Submitted in partial fulfillment of

the requirement of the degree

Master of Science in Biomedical

at

The City College of New York

of the

City University of New York

by

MARTA ISABEL VANEGAS ARROYAVE

April 2013

Approved:

_________________________________

Professor Simon Kelly, Advisor

_________________________________

Professor John M. Tarbell, Chairman

Biomedical Engineering Department

ii

If the brain were so simple that we could understand it, we would be so simple we couldn't.

— Emerson M. Pugh

iii

ACKNOWLEDGEMENTS

I would like to express my very great appreciation to Professor Simon Kelly for his

constant guidance and advice during the course of this project, and for giving me the opportunity

of being part of the Neural Engineering Lab. He has provided me with extraordinary tools to

enrich my scientific knowledge, and through challenging and encouraging discussions in the

field, has made of my motivation and expectations to continue increasing every day.

My grateful thanks are also extended to Dr. Annabelle Blangero, my research supervisor,

for sharing her exceptional expertise in the field of Neuroscience. Her patient guidance, useful

critiques and valuable comments throughout the development of my research project, have been

remarkable in my formation.

Special thanks to Professor Lucas Parra and Professor Marom Bikson, also committee

members, for their excellent teaching and inspirational role during these two academic years, and

for generating fructiferous discussions about my research project.

Finally, I wish to thank my lab mates in the Neural Engineering Lab, friends, and family,

who have supplied me with a great environment for learning.

iv

Contents

List of figures ...................................................................................................................... v

Abstract .............................................................................................................................. vi

1 Introduction .................................................................................................................. 8

Study 1. ........................................................................................................................... 9

Study 2. ......................................................................................................................... 13

2 Study 1: Exploiting individual primary visual cortex geometry to boost steady state

visual evoked potentials ................................................................................................................ 15

2.1 Methods ........................................................................................................... 15

2.1.1 Subjects ........................................................................................................ 15

2.1.2 Stimuli ........................................................................................................... 15

2.1.3 Data acquisition ............................................................................................ 18

2.1.4 Multifocal mapping ...................................................................................... 19

2.1.5 SSVEP power measurement ............................................................................ 22

2.2 Results ................................................................................................................. 23

2.3 Discussion ........................................................................................................... 25

3 Study 2: Direct electrophysiological measurement of visual surround suppression in

humans 28

3.1 Methods ........................................................................................................... 28

3.1.1 Subjects ......................................................................................................... 28

3.1.2 Stimuli .......................................................................................................... 28

3.1.3 Data acquisition ............................................................................................ 30

3.2 Results ................................................................................................................. 30

3.2.1 Statistical analysis............................................................................................ 35

3.3 Discussion and future work ................................................................................. 36

References ......................................................................................................................... 38

v

List of figures

Figure 1 Study 1, the principle ...................................................................................................... 12

Figure 2 Study 1, flicker phase assignment schemes across the four SSVEP conditions of

stimulation..................................................................................................................................... 17

Figure 3 Study 1, pattern-pulse multifocal VEPs extracted as a response to each of the 32

locations in the visual field for one representative subject. .......................................................... 21

Figure 4 Study 1, frequency spectrum for averaged SSVEP amplitude (left) with zoomed

peak at the stimulus flicker frequency of 21.25 Hz. ..................................................................... 24

Figure 5 Study 2, stimulus configurations. ................................................................................... 29

Figure 6 Study 2, contrast response functions corresponding to peripheral stimulation at 25Hz,

for three background contrasts and five foreground contrasts. ..................................................... 31

Figure 7 Study 2, SSVEP scalp topographies for group average over trials for each condition of

stimulation. Collapsed by spatially in-and-out-of phase and trials with foreground contrast above

20%, at the frequency of interest .................................................................................................. 32

Figure 8 Study 2, SSVEP amplitude for group average over trials for each condition of

stimulation, collapsed by spatially in-and-out-of phase ............................................................... 34

vi

Abstract

The steady-state visual evoked potential (SSVEP) is an electroencephalographic response to

flickering stimuli generated in significant part by activity in primary visual cortex (V1). SSVEP

signal-to-noise ratio is generally low for stimuli that are located in the visual periphery, at

frequencies higher than 20 Hz, or at low contrast. Because of the typical "cruciform" geometry of

V1, large stimuli tend to excite neighboring cortical regions of opposite orientation, likely resulting

in electric field cancellation.

In Study 1, we explored ways to exploit V1 geometry in order to boost scalp SSVEP amplitude

via oscillatory summation, by manipulating flicker-phase offsets among angular segments of a

large annular stimulus. We found that by dividing the annulus into standard octants, flickering

upper horizontal octants with opposite temporal phase to the lower horizontal ones, and left vertical

octants opposite to the right vertical ones, the normalized SSVEP power was enhanced by 202%

relative to the conventional condition with no temporal phase offsets. In two further conditions we

individually customized the phase-segment boundaries based on early-latency topographical shifts

in pattern-pulse multifocal visual-evoked potentials (PPMVEP) derived for each of 32 equal-sized

segments. Adjusting the boundaries between 8 phase-segments by visual inspection resulted in

significant enhancement of normalized SSVEP power of 383%, a further significant improvement

over the standard octants condition. An automatic segment-phase assignment algorithm based on

the relative strength of vertically- and horizontally-oriented multifocal VEP scalp potential

amplitudes produced an enhancement of 300%.

In Study 2, we applied the same principle to obtain more reliable measures of visual evoked

activity to obtain surround suppression measures. Here we report for the first time, a novel

vii

paradigm that exploits simple signal processing, sensory physiology and psychophysical evidences

in order to extract a direct index of surround suppression using EEG. Surround suppression effects

were tested for low and high flickering frequencies in two different configurations of a flickering

stimulus (foreground, FG) on a static surrounding pattern (background, BG): foveal, where the

stimulus was a unique central disc, and peripheral, where four discs were presented at symmetrical

locations around the horizontal meridian. We varied FG and BG contrast combinations and also

evaluated the influence of differences in spatial phase and orientation between the surrounding

pattern and the foreground. Across a population of sixteen healthy subjects, we found that the

foreground contrast response function was significantly suppressed in proportion with the contrast

of the background, and that, like psychophysical measures, this suppression effect was greater

when the background was oriented in parallel with the foreground than when it was orthogonal.

Suppression effects were also greater for the peripheral stimulus condition. This is the first

demonstration of a clear surround suppression effect in the visual evoked potentials of humans,

and paves the way for the first definitive measurement of the relative contributions of under-

inhibition and over-excitation to hyperexcitability in epilepsy.

Keywords: Visual evoked potentials, steady state visual evoked potentials, visual cortex,

flickering frequency, power, excitability, inhibition, epilepsy.

8

1 Introduction

Scalp electroencephalography (EEG) has been widely used as a media of data acquisition

to record electrical activity noninvasively from the human cerebral cortex over the scalp, as well

as induced activity attached to sensory stimulation. Its features of high time resolution compared

to other brain monitors allow recording neural activity occurring thousandths of a second after

the presence of an external stimulus. This, together with the cost effectiveness allows

implementing this system easily.

Visual evoked potentials (VEP) are extrinsically induced EEG potentials that appear as a

response to visual stimulation, and can be captured from scalp electrodes located over the

occipital lobe. An interesting phenomenon occurs when a stimulus is presented at a repetitive

rate, and as the retina is excited by a periodic visual stimulus at frequencies ranging from 3.5 Hz

to 60 Hz, the brain generates electrical oscillations at the same (fundamental) frequency or

multiple frequencies (harmonics) of the visual stimulus. The brain’s visual response reaches a

steady state, and generates an almost sinusoidal, phase-locked oscillation, to the presented visual

stimulus (Regan 1977), known as the Steady State Visually Evoked Potential (SSVEP; Regan

1989).

SSVEPs have many applications in neural engineering and neuroscience. Simply

by computing the Fourier Transform of EEG time segments, the magnitude and phase spectra of

the SSVEP can be obtained in the frequency domain. The peak amplitude or power at the

frequency of stimulation provides a robust measure of the intensity of sensation for the stimulus

"tagged" by that frequency. For that reason, SSVEPs popularly serve as inputs to gaze-

dependent (Wang et al 2005, Gao et al 2003) and gaze-independent (Kelly et al 2005a, b; Allison

9

et al 2008) brain-computer interfaces (BCI), allowing communication between motor impaired

patients and their environment (Wolpaw et al 2002; Allison et al 2007), and have been a highly

useful tool in cognitive neuroscience research. For example, SSVEPs provide a measure of the

modulation of visual activity by spatial (Morgan et al 1996, Muller et al 2003, Lauritzen et al

2010), feature-based (Anderson and Muller 2010) and intersensory (Saupe et al 2009) attention,

and a means to track sensory evidence over time during perceptual decision formation

(O'Connell et al 2012).

As with all signals in human electrophysiology, the SSVEP is enveloped in a

considerable amount of noise, greatly limiting our ability to obtain robust measurements on a

single-trial basis. The problem of noise is exacerbated in conditions under which SSVEP

amplitude is low, such as when the flicker frequency is high (>20 Hz), when stimuli are in the

visual periphery, and/or when low contrast stimuli are used. These very conditions are of

significant interest in many lines of basic and applied vision research, leading to an imperative to

improve SSVEP signal-to-noise ratio (SNR). Strides on this front have been made especially in

the BCI field. Some studies have employed individual optimization of stimulus parameters such

as temporal and spatial frequency to maximize SNR (Lopez-Gordo et al 2011). Other studies use

more complex multivariate transformations or array decompositions to increase the SNR of the

SSVEP (Cichocki et al 2008).

Study 1.

The aim of Study 1, was to quantify the improvement in SSVEP SNR attained solely by

exploiting individual primary visual cortex (V1) geometry, without the use of any signal

10

transformations beyond the standard Fast Fourier Transform (FFT). As the first cortical area that

receives retinal input, V1 is thought to contribute significantly to the amplitude of the SSVEP on

the scalp, and the results of source localization studies support this notion (e.g. Di Russo et al

2007; Lauritzen et al 2010). In humans, area V1 lies predominantly on the medial occipital

cortical surface, covering an area that includes the calcarine sulcus and its outer banks. Its

geometry and retinotopic organization generally follows a well-known "cruciform"

configuration, whereby the upper and lower horizontal field octants of visual space project to the

floor and ceiling of the contralateral calcarine sulcus, respectively, while the upper and lower

vertical field octants project to the ventral and dorsal medial surface on the lips of the calcarine

sulcus (Holmes 1945; Jeffreys and Axford 1972; see Figure 1a, b). As a result of this retinotopic

organization, certain neighboring regions of space project to neighboring but oppositely-oriented

sections of the cortical surface (Figure 1b). Thus, when facing regions are simultaneously

activated, their electric fields will tend to cancel out. However, if oscillatory responses in these

oppositely-facing regions are driven with opposite phase, constructive interference would be

predicted to occur and produce larger SSVEPs on the scalp (Figure 1c, d).

In order to apply this constructive interference principle to enhance the SSVEP,

we imposed temporal phase offsets among angular segments of a flickering annular pattern

stimulus and compared the resultant amplitude to a "standard" condition in which the entire

annulus flickered temporally in phase. In a first test condition (“symmetric”), we assumed a

perfectly symmetrical, ideal cruciform configuration for all subjects as depicted in figure 1a,b, in

which V1 is divided symmetrically into horizontal and vertical parallel-facing segments and

discrete turns in the cortical surface correspond to polar angles at 45-degree increments in space.

The upper horizontal octants were flickered with opposite temporal phase (180°) relative to the

11

lower horizontal octants, and similarly the left vertical octants were flickered opposite to the

right vertical octants, so that constructive interference should occur for both the vertically-

oriented and horizontally-oriented dipolar electric fields.

In two further test conditions, we attempted to account for differences in visual

cortex geometry across individuals. It is well known that area V1 varies widely in anatomical

shape and extent (Brindley 1972, Stensaas et al 1974). Variability in cortical folding patterns and

the distribution of V1 within the calcarine sulcus contributes substantially to subject-to-subject

variability in the topographical variations of early VEP responses with respect to stimulus

location (Jeffreys and Smith 1979, Butler et al 1987, Clark et al 1995). To characterize individual

V1 geometry, we performed multifocal pattern-pulse stimulation at 32 radial segments of the

annular stimulus. The pattern-pulse multifocal visual evoked potential (PPMVEP) is a technique

that enables the simultaneous derivation of pattern-onset VEPs from multiple visual field

locations by presenting orthogonal discrete pulse trains (James 2003) at each location. The

resultant VEPs strongly depend on the retinal location of the stimulus and inherent anatomical

differences across subjects. For our current purposes, we took the amplitude in the early time

range of 80-90 ms of the PPMVEP as an index of V1 activity (Baseler et al 1994; Slotnick et al

1999; Fortune et al 2009). We assigned flicker phase-offsets to segments of the SSVEP stimulus

in two ways: first, scalp topographies were visually inspected with reference to the typical source

geometry of the cruciform model, and boundaries of the standard octants were adjusted to align

with polar angles in the visual field where characteristic polarity inversions and topographical

shifts of the VEP occurred (“tailored octants” condition). Second, phase offsets were computed

using an automatic computer-based algorithm assigning each of the 32 segments to one of 4

phases (0°, 90°, 180°, 270°) based on the signs and relative magnitudes of horizontal and vertical

12

dipolar PPMVEP components (“auto-phase assignment” condition). We quantified

improvements in SSVEP SNR simply by calculating spectral power at the frequency of

flickering in the standard Discrete Fourier Transform.

Figure 1 Study 1, the principle

a- Visual field divided into symmetrical octants, paired with the ideal topographical

distributions of the initial component (“C1”) of the transient VEP that would result from discrete

stimulation at each location. Negative(-) and positive(+) scalp topographies represent the polarity

of the initial VEP component. b- Coronal view of the calcarine sulcus located on the medial

surface of the occipital lobe, illustrating the cruciform model of V1. Each arrow depicts

electrical dipole orientation as a result of stimulation at the location in visual space of the

corresponding color. c- Signal cancellation as a result of destructive interference between

opposite dipoles being stimulated with temporally in-phase flicker stimuli. d- Signal summation

as a result of constructive interference between opposite dipoles being stimulated temporally out-

of phase.

13

Study 2.

Mammals visual cortex and its complex circuitry have been explored from decades ago

using electrophysiology tools such as single unit recordings (Hubel 1959, Hubel and Wiesel

1959). Animal models like cat, monkey and mouse have facilitated the understanding of

neurophysiological mechanisms corresponding to visual events (Hubel and Wiesel 1962, Daniel

and Whitteridge 1961, Dräger 1975). As a result, visual system features such as neuronal tuning

to pattern orientation, neuronal selectivity to stimuli in the receptive field, extraclassical (or non-

receptive field) receptive field phenomena, and contrast normalization have been elucidated by

means of firing rate estimation (Allman et al 1985, Gilbert and Wiesel 1990, Sato et al 1995),

leading to the advancement of quantitative models describing neuronal computations (Carandini

et al 1997, Tolhurst et al 1997, Adorján et al 1999).

One of the most remarkable features of the mammalian visual system is surround

suppression. This refers to the modulation of neuronal responses to a stimulus presented within

the receptive field (RF) by the presence of a surrounding stimulus. Surround suppression effects

have been identified to be generated at earlier stages of the visual system, where

electrophysiology has provided with significant contributions in the field. Single-unit recordings

from visual cortex in monkey and cat have shown evidence of this neuronal mechanism in

primary visual cortex (V1) (Gilbert and Wiesel 1990, Cavanaugh et al 2002a, Cavanaugh et al

2002b, Levitt and Lund 1997). Implementation of psychophysics tools to determine the

relationship between stimulus and sensation (Gescheider 1997) has been crucial in the study of

surround suppression in humans, contributing towards articulation of several explanations and

creation of comprehensive theories about the phenomenon of visual surround suppression. From

14

animal studies, neuronal tuning occurs at different spatial frequencies and pattern orientation. In

a similar way, this phenomenon has been well established in psychophysics, where the influence

of a variety of stimulus factors have been demonstrated to strengthen or reduce the surround

suppression modulation, including similarity of features between the “foreground” (center) and

“background” (surround), spatial frequency and relative orientation (Chubb et al 1989, Xing and

Heeger 2000).

Such behavioral indices have been successfully employed in clinical studies to implicate

inhibitory dysfunction in schizophrenia (Dakin et al 2005). Surround suppression effects have

also been demonstrated in functional imaging work, in which it was established that it is

expressed most strongly in V1 (Zenger-Landolt and Heeger 2003). However, up until now,

technical challenges have precluded its direct measurement in non-invasive EEG, which is

inarguably the most clinically practical recording modality. In this study, we report a novel

paradigm that exploits simple signal processing in conjunction with anatomical and

physiological properties of early visual cortex, sensory physiology and psychophysical principles

in order to extract a direct index of surround suppression using non-invasive human

electroencephalography (EEG).

15

2 Study 1: Exploiting individual primary visual cortex geometry to boost steady state

visual evoked potentials

2.1 Methods

2.1.1 Subjects

EEG data were recorded from 16 healthy subjects between 22 and 32 years old (8 female). All

participants reported normal or corrected-to-normal vision and no history of neurological

disorders. Informed consent was obtained before their participation, and all experimental

procedures were approved by the Institutional Review Board of The City College of New York.

2.1.2 Stimuli

The procedure was conducted inside a dark, soundproof and radio frequency interference (RFI)

shielded room. Stimuli were presented on a gamma-corrected CRT monitor (Dell M782) with a

refresh rate of 85Hz and 1280x1024 pixels of resolution. Stimuli were presented dichoptically at

a viewing distance of 57 cm. The background (middle) luminance was fixed at 64.39cd/m2 after

estimating the gamma correction curve of luminance. Our stimulus presentation was

programmed in a commercial software package (MATLAB 6.1, The MathWorks Inc., Natick,

MA, 2000), with the PsychToolbox extension (Brainard 1997, Pelli 1997). A small white square

was presented at the center of the screen during the full length of the experiment, as a fixation

spot. Subjects were instructed to maintain fixation on this spot throughout each block, and this

was monitored continually by the experimenter.

16

In each of the four stimulation conditions we presented a 100%-contrast, annular

checkerboard stimulus with inner radius of 3° and outer radius of 10°, flickering at 21.25 Hz. We

chose this frequency because it avoids highly reactive bands such as alpha and, as mentioned in

the introduction, SNR is known to be lower at higher frequencies. The checkerboard was

composed of 64 x 7 checks in total (polar angle x eccentricity). The conditions varied only in the

way temporal phase was offset among radial segments of the pattern:

1) Standard. As a baseline comparison condition, we used a conventional configuration

in which the entire annulus flickered temporally in phase (Figure 2a).

2) Symmetric. This configuration assumes a perfectly symmetric brain anatomy in which

the calcarine sulcus contains ideally parallel-facing segments that perfectly map onto the

horizontal octants. The annular checkerboard was divided into eight 45-degree segments, and

the upper horizontal angular octants were flickered with opposite temporal phase relative to the

lower horizontal ones. In the same manner, the left vertical octants were flickered with opposite

phase relative to the right vertical ones (Figure 2b).

3) Tailored Octants. We attempted to account for variability in visual cortex geometry

across individuals. To characterize individual geometry, we performed pattern-pulse multifocal

stimulation of 32 radial segments of the annular stimulus derived from shifted versions of an

original binary m-sequence (Baseler et al 1994; James 2003; see below section 2.4.). In the

Tailored Octants condition, the 32 topographic maps were associated with approximate source

orientations by visual inspection, using the standard cruciform model as a reference (Figure

1a,b). We adjusted the boundaries of the 8 octants to coincide with the transitions in PPMVEP

topography from midline-focused to lateralized (shift from horizontally to vertically-oriented

cortical surface), and from positive to negative polarity (shift from ceiling to floor of calcarine,

17

or from left to right hemisphere). An individually-characterized visual stimulus configuration

was thus formed, with the same flicker-phase assignments as the symmetric condition but

without the constraint of equal size across the 8 segments (Figure 2c; see also Figure 3c for

individual example).

4) Auto-Phase Assignment. From the same scalp topographic maps extracted from the

PPMVEP, an automatic computer-based algorithm assigned each of the 32 segment responses to

one of 4 phase offsets: 0°, 90°, 180° and 270°. In this case, there were intermingled segments

flickering at shifted temporal phases (Figure 2d). The details of this algorithm are provided

below.

Figure 2 Study 1, flicker phase assignment schemes across the four SSVEP conditions of

stimulation.

The flicker frequency was 21.25 Hz in all cases. a- The Standard stimulus is the

conventional configuration used for measuring SSVEPs whereby the entire annulus flickers in-

phase (0° shift). b- Symmetric stimulus with segment boundaries placed at 45-degree

increments in polar angle. Light and dark red octants are flickered with a temporal phase offset

(180° shift) that leads to constructive interference on the scalp. In the same manner, the light and

dark green octants are driven in opposite phase. c- Tailored Octants configuration based on scalp

topographies obtained from the multifocal mapping. This appears highly similar to the standard

octants condition but with boundaries adjusted to coincide with cruciform-consistent shifts in

topography. Note for example that in this representative subject, whose PPMVEP topographies

are shown in figure 3c, the transition from midline-negative to midline-positive topographies

occurs one segment below the horizontal meridian in the right visual field. d- The automatic

phase-assignment condition relies on an algorithm that assigns one of 4 possible phase offsets to

18

each of the 32 segments based on the relative amplitude of horizontally-oriented and vertically-

oriented neural response components.

For all subjects the PPMVEP mapping procedure was administered first, lasting

approximately 5 minutes, before any SSVEP recording. The SSVEP conditions were presented

in separate blocks, which were run in counterbalanced order across subjects according to a Latin

square. Each block contained 5 trials of the same SSVEP condition, each of them lasting 24

seconds resulting in a block duration of approximately 3 minutes. To avoid tiredness and eye

fatigue, the subjects had a break of 15 minutes between the multifocal mapping and SSVEP

testing. The total recording time was always less than 1 hour.

2.1.3 Data acquisition

EEG data were recorded from a 64-channel montage using Brain Products DC amps and the

actiCAP system (Oostenveld and Praamstra 2001) with an online reference at standard site FCz.

Data were collected at a sample rate of 500 Hz with an online notch filter at 60 Hz, and high-pass

filter with 0.5-Hz cut-off. Impedances were stable below 30 kΩ. Data were analyzed offline in

Matlab using in-house scripts in conjunction with data reading routines and topographic mapping

functions of EEGLAB (an open source toolbox for EEG analysis; Delorme and Makeig 2004).

Offline, we applied a band-pass Butterworth digital filter (4th order) with cut-off frequencies 1

and 45 Hz.

19

2.1.4 Multifocal mapping

The multifocal VEP technique enables the simultaneous derivation of VEPs from multiple visual

field locations by applying orthogonal stimulus waveforms to drive phase-reversal (Baseler et al

1994; Slotnick et al 1999) or discrete pulse presentation (James 2003) at each location. The

resultant VEPs strongly depend on the retinal location of the stimulus and inherent anatomical

differences across subjects. Topographical maps of the initial VEP component (Figure 3c) thus

provide a reliable window on the reversals and turns of the cortical surface and their

corresponding boundaries in the visual field.

To establish how scalp topographies for a given individual vary as a function of polar

angle in the visual field, we used the same annular checkerboard stimulus divided into 32

segments covering 3 to 10° of eccentricity and each spanning 11.25° of polar angle (π/16 rad, 2 x

7 checks). We converted an original binary m-sequence of length 1024 (composed of transitions

between 1 and -1, with equal total time spent on each level) to a pulse train by designating every

-1 to 1 transition as a pulse and setting all other frames to zero. We then interposed 3 blank

frames between every frame to create a pulse sequence of 4096 frames, lasting 48s. Because the

32 pulse trains are orthogonal, visual evoked potentials can be estimated by deriving the impulse

response function of the visual system to each segment using multiple linear regression (James

2003, Baseler et al 1994).

To assign phase offsets to stimulus segments based on these PPMVEP data in the “Auto-

phase assignment” condition, we assumed a set of four distinct, ideal topographies (see Figures

1a and 3c) associated with the cruciform model, and matched each of the 32 measured

topographies for a given individual to one of the ideal topographies. We computed topographic

maps in the typical time range of the peak of the initial "C1" component of the VEP, 80 to 90 ms

20

(Figure 3a; see e.g. Clark et al 1995). For the purposes of the present study, we took the scalp

potential in this time window to reflect mainly V1 activity, in keeping with previous work using

multifocal paradigms (e.g. Baseler et al 1994; Slotnick et al 1999; Park et al 2008; see Ales et al

2010, 2013 and Kelly et al 2012 for a fuller discussion). In the Tailored octants condition, we

adjusted segment boundaries simply by visual inspection of these topographies. For the Auto-

Phase Assignment condition, we decomposed this early visual response component into

horizontal and vertical components using a simple Cartesian coordinate system in order to

capture activity from vertically- and horizontally-oriented sections of the cortical surface,

respectively. Specifically, we derived a horizontal “H” signal by subtracting right hemisphere

electrodes from left hemisphere, and derived a vertical "V" signal by referencing a cluster of

dorsal midline electrodes to the average mastoids (Figure 3b). We applied a simple criterion

whereby if the V component was of greater absolute magnitude than the H component for a

particular location, then its phase offset was assigned as 0° if negative and 180° if positive,

regardless of its specific location in the visual field (see Figure 3c). Similarly, if the H

component was greater than the V component, then its phase offset was assigned as 90° if the

positive pole lay over the right hemisphere and 270° if the positive pole lay over the left. Figure

3b,d shows an example subject to demonstrate this phase assignment principle. Notice for

example that this subject exhibited a strongly lateralized topography for location 17, consistent

with a large and flat calcarine fundus in his right hemisphere; this is not accounted for in the

standard or tailored 8-octant stimulus segmentation, whereas the automatic algorithm assigned

this location to the 90° phase group, consistent with horizontal dipolar activity. This subject’s

phase assignments are depicted in figure 2d.

21

Figure 3 Study 1, pattern-pulse multifocal VEPs extracted as a response to each of the 32

locations in the visual field for one representative subject.

a- PPMVEP waveforms from a single electrode in a single subject for all 32 locations,

showing the time window in which the topographic maps were computed (80 to 90 ms). b-

Vertical (V) and Horizontal (H) signals were extracted from the indicated pairs of electrode sets.

These bipolar signal components were used both in the PPMVEP-based automatic phase

22

assignment algorithm and in the measurement of SSVEP power in the main SSVEP stimulation

conditions. c- Topographies of the earliest PPMVEP component for another representative

subject, showing clear variations according to the location of the stimulus in the visual field. In

the Tailored Octants condition, octant boundaries were determined by eye, and are indicated for

this subject by brackets stemming from each corresponding ideal topography. In the Auto-Phase

Assignment condition, phase offsets were assigned to segments automatically on the basis of the

simple classification of H and V components in the coordinate system shown in the inset. Early

signal amplitude for the "V" bipolar signal (red electrodes minus orange) is plotted against "H"

amplitude (blue minus green) for each of the 32 segments. The dashed diagonal lines mark the

boundaries between the four phase classes.

2.1.5 SSVEP power measurement

In each of the four SSVEP stimulation conditions, we first derived H and V signals from the

bipolar electrode clusters indicated in figure 3b. Specifically, we derived a horizontal “H” signal

by subtracting right hemisphere electrodes from left hemisphere, and derived a vertical "V"

signal by referencing a cluster of dorsal midline electrodes to the average mastoids. The H and V

signals were added together in each condition, and the FFT was computed on this combination to

measure SSVEP power in 6 non overlapping epochs of 4s, covering the full 24-s duration of each

trial. We rejected epochs containing artifacts such as blinks and eye movements by setting a

threshold of 80µV for the maximum minus minimum of each epoch. This resulted in a mean±SD

of 6.25±7.40 rejected epochs out of 30 total per subject. A phase lag was imposed between the H

and V signals before summation, and in an identical manner for all conditions, the phase lag

resulting in the highest SSVEP power was used. FFT spectra were normalized by dividing by the

average power in the band 15-20Hz for each subject.

23

2.2 Results

Figure 4 shows the grand-average normalized power spectrum for each of the four

stimulation conditions. The alpha band is included in the frequency scale so that its power may

serve as a visual benchmark. A striking enhancement in SSVEP power at 21.25 Hz is evident for

all three test conditions. It is important to emphasize that across all four conditions, the identical,

bare minimum of analysis was performed – simply an absolute-squared FFT – and that the only

difference across the conditions was that phase offsets were imposed among flickering segments

of the same stimulus. The average percentage increase in normalized power for the Symmetric,

Auto-Phase Assignment and Tailored Octants conditions relative to the Standard condition was

202%, 300% and 383%, respectively. We submitted the normalized SSVEP power values to a

one-way ANOVA to test for significance. There was a significant main effect of stimulation

condition (F(3,45)=9.03, p<0.001). Follow-up, pairwise comparisons revealed that the

enhancement for all three test conditions relative to the Standard condition was significant

(Symmetric t(15)=3.59, p=0.0013; Tailored Octants t(15)=4.47, p=0.0002; Auto-Phase

Assignment t(15)=3.27, p=0.0026), and that the further improvement in both the Tailored

Octants (t(15)=2.38, p=0.016) and Auto-Phase Assignment (t(15)=1.95, p=0.035) relative to the

Standard Octants was also significant.

It is interesting that the enhancement we observed in the condition where “octant”

boundaries were tailored by visual inspection performs as well as a condition using an automatic

algorithm to assign phase offsets to segments in a relatively unconstrained manner. The highly

simplified criterion used by the algorithm for automatic phase assignment can no doubt be

improved upon. Nevertheless, the competitive performance of both the symmetric and tailored

octants conditions suggests that even without multifocal mapping, SSVEP power could be

24

considerably enhanced simply by applying a fixed phase-segment configuration for all subjects.

To establish such a configuration that potentially generalizes across the population, we averaged

boundaries over subjects in the tailored-octants condition. The grand-average boundaries were

located at polar angles of -11.25°, -56.25°, -90°, -123.75°, -168.75°, 33.75°, 90°, 135°, relative

to the horizontal meridian of the right visual field. This is consistent with the estimates of

average cortical folding points in a previous study of the initial “C1” component of the VEP

(Clark et al 1995).

Figure 4 Study 1, frequency spectrum for averaged SSVEP amplitude (left) with zoomed

peak at the stimulus flicker frequency of 21.25 Hz.

Asterisks represent p values: * p<0.05, ** p<0.005 and *** p<0.00005. Individual

SSVEP power values for all subjects and stimulus configurations on the same scale (right). For

one of the subjects, the SSVEP power is above the plot limits (130.4 V2). The red line

corresponds to the grand-average at each condition. Sd = Standard; Sym = Symmetric; APA =

Auto-Phase Assignment; TO = Tailored Octants

25

2.3 Discussion

In this study we have shown that, solely by manipulating the relative phase of flicker among

segments of a fovea-centered stimulus, the SSVEP can be significantly enhanced relative to

conventional paradigms. This has significant implications in particular for any basic or applied

research that relies on single-trial estimates of SSVEP power. Trial-to-trial variations in the

magnitude of perceptual signals such as the SSVEP can be leveraged to illuminate the neural

mechanisms of vision and cognition. For example, the SSVEP comprises a highly robust

"sensory evidence" signal in decision making tasks based on contrast judgments (O' Connell et al

2012). Robust SSVEP measurement is also crucial for BCI applications, which depend on

momentary frequency-tagged SSVEP amplitudes to decipher the current focus of overt (e.g. Gao

et al 2003) or covert (e.g. Kelly et al 2005a,b) attention. However, because the current approach

works by integrating across sub-regions of a single stimulus, its utility in applications such as

these visual BCIs, where isolated visual responses from multiple discrete locations are required,

remains unclear. The approach clearly works well as an assay of non-spatially-specific early

visual cortical responses and their evolution over time.

The central feature of our approach is that typical or individually-defined cortical

anatomical organization in primary visual area V1 is estimated and exploited to construct stimuli

that promote oscillatory summation on the scalp. Many previous studies using source

localization algorithms have identified V1 as a major cortical generator of the SSVEP (e.g. Di

Russo et al 2007; Lauritzen et al 2010). However, in the same studies, additional extrastriate

generators are identified as well. Our approach is based on the notion that because of V1’s

retinotopic organization, certain regions of space project to neighboring but oppositely-oriented

pieces of cortex, so that in-phase activation of certain pairs of locations will result in electric

26

field cancellation, while opposite-phase activation will result in constructive interference. But

this feature of polarity reversal is not at all unique to V1, and insofar as extrastriate areas such as

V2 and V3 are following the high-frequency flicker stimulus, these areas may also contribute

significantly to the enhancement we have observed. In functional retinotopic-mapping-informed

simulations of the scalp distributions predicted by stimuli in individual regions V1, V2, V3, Ales

and colleagues (2010, 2012) have shown striking upper-field to lower-field polarity inversion for

areas V2 and V3 that in fact appears better aligned than that predicted for V1. It is thus possible

that though our approach was based on a model of V1 organization only, areas beyond V1

contribute as well. Further work will establish the relative contributions of the areas.

It is worth re-emphasizing the elementary nature of our approach. Depending on the

pattern, shape, and location of an SSVEP-eliciting stimulus, different current sources may be

active at the same time, at different sites and directions in the primary visual cortex, according to

the cruciform model of V1. Consequently, multiple and varied sources must not be described as

a unique dipole, but as a group of dipoles that once combined appear as oscillatory activity on

the scalp. This response is in most cases embedded in noise, and its SNR is very low. Methods

for characterizing and localizing EEG dipolar sources are typically based on automated

algorithms, for example, those that involve a conductor model, a group of dipole sources located

within the modeled brain, and the association of such configurations with theoretical scalp

potentials (Sun 1997). Our approach avoided such detailed models and approximations, and

rather based the stimulus configurations on the well-known anatomy of V1, and characterization

of the ideal scalp potential topographies. Further improvement is likely to be attained by

employing more complex modeling and constrained optimization routines, as well as

implementation of alternative referencing approaches such as reference electrode standardization

27

technique (REST, Qin et al 2010), individualized electrode selection (Wang et al 2004), and

multiple frequency stimulation coding (Zhang et al 2012)

The present approach may also prove to be valuable in clinical applications. For example,

SSVEP signal strength has been used as a measure of excitability in visual cortex to determine

indices of contrast gain control in generalized epilepsy (Porciatti et al 2000, Tsai et al 2011).

Our stimulus configuration holds promise in providing a more robust measure of excitability, not

only because of the improved SNR we have demonstrated here, but also because high-frequency

SSVEPs above 20 Hz may be less vulnerable to confounding activity in the most volatile parts of

the EEG frequency spectrum such as the alpha band.

On a technical note, SSVEP phenomena have been widely explored for different spatial

frequencies, temporal frequencies, degrees of eccentricity, contrast levels, shapes, geometries

and colors. Our study marks the first time that SSVEP signal-to-noise ratio is enhanced by

means of implementing a visual stimulus that follows an unconventional configuration aimed to

activate opposite dipole sources in V1. More generally, our results provide evidence that dipole

cancellation and dipole summation is evidently expressed in scalp potentials and should be taken

into account in stimulus design.

28

3 Study 2: Direct electrophysiological measurement of visual surround suppression in

humans

3.1 Methods

3.1.1 Subjects

EEG data were recorded from sixteen healthy subjects between 22 and 32 years old (7 female).

All participants reported normal or corrected-to-normal vision and no history of neurological

disorders. Informed consent was obtained before their participation, and all experimental

procedures were approved by the Institutional Review Board of The City College of New York.

3.1.2 Stimuli

The procedure was conducted inside a dark, soundproof and radio frequency interference (RFI)

shielded room. Stimuli were presented on a gamma-corrected CRT monitor (Dell M782) with a

refresh rate of 100Hz and 800x600 pixels of resolution. Stimuli were presented dichoptically at

a viewing distance of 57 cm. Our stimulus presentation was programmed in a commercial

software package (MATLAB 6.1, The MathWorks Inc., Natick, MA, 2000), with the

PsychToolbox extension (Brainard 1997, Pelli 1997). A small white square was presented at the

center of the screen during the full length of the experiment, as a fixation spot. Subjects were

instructed to maintain fixation on this spot throughout each block, and this was monitored

continually using the EyeLink 1000 (SR-Research) eye tracker.

29

Surround suppression effects were measured using steady state visually evoked potentials

(SSVEP) elicited by the flickering of a “center” stimulus on a static “surround”. We assessed

foveal and peripheral surround suppression by presenting a single disc of radius 2 deg in the

center of the screen, or four symmetric discs, respectively, cut out of a full-screen vertical

sinusoidal grating of spatial frequency 2 cpd (Figure 5a). Peripheral discs were positioned at pre-

assigned locations, and were flickered according to previous research where we demonstrated a

principle to improve SSVEP SNR, where upper horizontal discs are flickered with opposite

temporal phase relative to the lower horizontal ones (Vanegas et al 2013, and study 1 above).

Low and high flickering frequencies (7.2Hz and 25Hz) were tested.

Figure 5 Study 2, stimulus

configurations.

a. Foveal and peripheral

stimuli, with a foreground of contrast

100% on a background of contrast 0%.

b. Foreground stimulus flickered on a

static background, and varied across

five contrast levels 0, 25, 50, 75 and

100%. Background surround contrast

was also changing across three levels

0, 50 or 100%. A foreground of 100%

on a background of 50% contrast,

spatial in-phase is shown. c. Effects of

center-surround feature similarity

were assessed by presenting spatially

out-of-phase surrounding patterns (left

and middle), and orthogonal (right).

30

The definition of contrast for the sinusoidal pattern was done according to the

conventional metric of Michelson contrast: 𝐿𝑚𝑎𝑥−𝐿𝑚𝑖𝑛

𝐿𝑚𝑎𝑥+𝐿𝑚𝑖𝑛, where Lmax refers to the maximum

luminance, and Lmin to the minimum. Luminance on our monitor ranged from 0.01 to 130 cd/m2,

and was measured using a photometer. We varied the “center” contrast across five levels: 0, 25,

50, 75 and 100% (Figure 5b), to generate individual contrast response functions corresponding to

three “surround” contrast levels: 0, 50 and 100%, and also evaluated the influence of center-

surround feature similarity by parallel surrounding grating patterns spatially out-of-phase, as well

as orthogonal (Figure 5c). A total of fourteen conditions were presented randomly in trials of 2.4

seconds long each. Six trials per condition were recorded.

3.1.3 Data acquisition

EEG data were recorded at a sample rate of 500Hz from a 96-channel montage of electrodes with

an online reference at standard site FCz, other details as they were described in Study 1.

SSVEP amplitude was measured from vertically-oriented dipoles in the calcarine sulcus

by referencing dorsal midline channels referenced (by simple subtraction) to average mastoids.

We computed a Fast Fourier Transform (FFT) for 2-s windows in each trial and averaged across

trials in each condition.

3.2 Results

We calculated the grand-average SSVEP amplitude for each of the stimulation conditions

(fourteen in total), and plotted the contrast response functions for each background contrast level.

31

In figure 6, we derived the main contrast response function for peripheral stimulus, at

electrode POz in these cases. Black traces depict contrast response functions (Figure 6)

associated to a foreground stimulus embedded on a zero contrast background (mid-gray, Figure

5a -right). Notice the increment of SSVEP amplitude linked to increasing foreground contrasts.

As the background contrast increases from 0% to a background contrast of 50%, the SSVEP

amplitude diminishes (Figure 6, brown traces). Similarly, for the highest background contrast -

100%-, the SSVEP amplitude was greatly decreased (Figure 6, light orange traces)

Figure 6 Study 2, contrast response functions corresponding to peripheral stimulation at

25Hz, for three background contrasts and five foreground contrasts.

a- Orthogonal background, b- Spatial out-of-phase background, c- Spatial in-phase

background

Scalp topographies were derived also from the group average over trials for each

condition of stimulation (foreground contrast > 20%) and frequency of interest. They were

subsequently collapsed by spatially in-and-out-of phase, given their similarity. We found that the

spatial distribution of SSVEPs over the scalp differed for peripheral stimulus and foveal

stimulus. Electrode POz and electrode Oz showed the highest SSVEP power for peripheral

stimulation, and foveal stimulation (Figure 7a,d and c,f ), respectively.

32

For this reason, SSVEP amplitude was selected from these two electrodes in order to generate

further contrast response functions (Figure 6 and Figure 8).

As we expected from Study 1, SSVEP SNR would increase by flickering the upper

horizontal discs with opposite temporal phase relative to the lower horizontal ones (tempOP

condition, Figure 7a,d). This was proved by presenting a control condition where the four discs

were flickering together, or temporally in-phase (tempIP condition, Figure 7b,e). In fact,

SSVEPs were boosted significantly.

Figure 7 Study 2, SSVEP scalp topographies for group average over trials for each

condition of stimulation. Collapsed by spatially in-and-out-of phase and trials with

foreground contrast above 20%, at the frequency of interest

a,b- Peripheral stimulus embedded on a foreground parallel to background parallel,

flickering at 25Hz, temporally out-of phase and temporally in-phase, respectively. c-Foveal

stimulus, foreground and background parallel, flickering frequency 25Hz. d,e- Peripheral

stimulus embedded on a foreground parallel to background parallel, flickering at 7.2Hz,

33

temporally out-of phase and temporally in-phase, respectively. f-Foveal stimulus, foreground and

background parallel, flickering frequency 7.2 Hz.

Assessment of visual surround suppression effects was done by looking at SSVEP

amplitude for foreground contrasts of 50 and 100%, and background contrasts of 0% (mid-gray)

and 100% (black and white stripes). Surprisingly, the suppression effects were higher for

peripheral stimulus compared to foveal stimulus (Figure 8). As it was expected from the

topographical analysis, SSVEP amplitudes for the temporally in phase stimulation condition

were very small and did not express any effect related to surround contrast for a flickering

frequency of 25 Hz (Figure 7b). There is a suppression effect at 7.2 Hz (Figure 7e).

In order to test for significance, we computed a repeated measures Analysis of Variance

(ANOVA).

34

Figure 8 Study 2, SSVEP amplitude for group average over trials for each condition of

stimulation, collapsed by spatially in-and-out-of phase

a,b- Peripheral stimulus embedded on a foreground parallel to background parallel,

flickering at 25Hz, temporally out-of phase and temporally in-phase, respectively. c-Foveal

stimulus, foreground and background parallel, flickering frequency 25Hz. d,e- Peripheral

stimulus embedded on a foreground parallel to background parallel, flickering at 7.2Hz,

temporally out-of phase and temporally in-phase, respectively. f-Foveal stimulus, foreground and

background parallel, flickering frequency 7.2 Hz.

35

3.2.1 Statistical analysis

Comparisons were made for a population of 16 subjects, between 14 conditions. Three separate

four factor ANOVAs were computed, with varying levels of each factor.

The first was a 2 x 2 x 3 x 5 ANOVA with the four factors: Background Orientation

(parallel, orthogonal), Spatial Phase (in, out), Background Contrast (0, 50, 100%) and

Foreground Contrast (0, 25, 50, 75, 100%). There was a significant main effect of foreground

contrast (F(4,60)=33.9, p<0.01), a main effect of background contrast (F(2,30)=21.6, p<0.005),

and a significant interaction between the two (F(8,120)=12, p<0.0005), reflecting the fact that the

contrast response function was reduced in proportion with the background contrast level. There

was a main effect of the background orientation (F(1,15)=28.2, p<0.0001), further explained by

an interaction between orientation and both background and foreground contrast (F(8,120)=3.46,

p<0.01), driven by a stronger suppression effect for the parallel background (spatially in and out

of phase).

The second one was a 2 x 2 x 2 x 2 ANOVA, where the four factors were Location

(Peripheral, Foveal), Flickering Frequency (25, 7.2Hz), Background Contrast (0, 100%) and

Foreground Contrast (50, 100%). We found a significant main effect of flickering frequency

(F(1,15)=12.9, p<0.005), and location (F(1,15)=7.59, p<0.05), reflecting a larger suppression

effect for a frequency of 25Hz and peripheral location.

In the third one, also a 2 x 2 x 2 x 2 ANOVA, the factors were Temporal Phase

(conventional in phase, temporal shift principle in Study 1), Flickering Frequency (25, 7.2Hz),

Background Contrast (0, 100%) and Foreground Contrast (50, 100%). A striking enhancement in

SSVEP amplitude at 25Hz due to the phase offsets among upper and lower discs of the same

stimulus can be appreciated in the topographies. Both overall SSVEP amplitude (F(1,15)=22.2,

36

p<0.0005) as well as its suppression due to background contrast (F(1,15)=23.4, p<0.0005) were

larger for opposite-phase stimulation. SSVEP amplitude was overall greater at 7.2Hz

(F(1,13)=38.9, p<0.001), but suppression effects did not differ from those at 25 Hz (p>0.1).

3.3 Discussion and future work

This study provides evidence of the principle presented in Study 1, where by manipulating the

relative phase of temporal flicker among upper and lower discs, the SSVEP can be significantly

enhanced relative to the conventional stimulus. Furthermore, this supports the fact that such

SSVEP enhancement guarantees a very robust measure with ample applications in clinical research

and neuroscience.

Our results are in agreement with previous research in animal studies based on single unit

recordings from visual cortex in monkey and cat (Gilbert and Wiesel 1990, Cavanaugh et al

2002a, Cavanaugh et al 2002b, Levitt and Lund 1997), showing evidence of lateral interactions

and inhibition mechanisms in V1. In our case, scalp potentials reflect the same facts, and even

though EEG is an external measure of cortical activity, support the same theories. In relation

with psychophysics, several studies have demonstrated modulation of perception in the presence

of a surround: suppression and facilitation (Chubb et al 1989, Xing and Heeger 2000). Therefore,

we were able to show the same center-surround interactions in the fovea and the periphery and

this guides our next step to estimate those indices using only psychophysics, and correlate

SSVEP amplitude with behavioral effects.

Here we used EEG as a mean to unlock the brain, and in ongoing work we are testing the

psychophysical effects in relation to our results, expecting that they will be correlated leading to a

way of assessing visual perception. Electrophysiological measures of surround suppression using

37

EEG enlighten a pathway to plenty of applications in the clinical field. Our results demonstrate

this is a reliable measure that will serve as an index of cortical excitation and inhibition.

Additionally, it is an advantage to be able to use flickering stimulus at high frequency ranges, ad

it is 25 Hz, further away from brain rhythms that might interfere with our metrics.

In relation to clinical applications, these paradigms will be tested in patients with

idiopathic generalized epilepsy in order to generate a theory about whether this neurological

disorder is associated to lack of inhibition or hyper-excitation. By measuring these indices in

patients under antiepileptic drugs, and before and after treatment, we would be able to

characterize such mechanisms.

Currently, two models describing the surround suppression effect are based on

Difference-of-Gaussians and Ratio-of-Gaussians (Angelucci and Bressloff 2006). Although our

measures are not comparable to a neuronal level, we are looking forward to integrate such

models with our metrics, into a global substractive-divisive model that yields to quantitative

estimates of excitation and inhibition.

The phenomenon of surround suppression appears also as a way of estimating the extent

to which attention can modulate perception, and can increase contrast sensitivity. Several studies

have shown the influence of attention in contrast perception using psychophysics (Carrasco

2011, Barbot et al 2012), as well as neuronal modulation as observed in recordings from monkey

V1 (Ito and Gilbert 1999, Kapadia et al 1995). Therefore the idea of using surround suppression

as a way of estimating how much the brain can improve the appearance, or how much can we

increase sensitivity at a specific spot rises from the outcomes of this study.

38

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