Steady-state Visual Evoked Response Research Articles

A hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG

While recent developments in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have enabled a bridge between the brain and external devices with relatively high communication speed, there is still room for improvement. Notably, the phenomenon of “BCI illiteracy,” which refers to the 15%–30% of people who struggle to type or control devices using BCI, remains unsolved, limiting the practical application of BCI systems. The EEG-based BCIs performance is constrained by the low-quality scalp EEG signals due to the attenuation and distortion of the skull. To address these limitations, this study proposes a hybrid BCI system combining EEG with magnetoencephalogram (MEG), a neuroimaging technology not influenced by the volume conduction effect, to boost BCI performance by enhancing signal quality. Comparative experiments involving 22 subjects showed that the steady-state visual evoked response (SSVER) from MEG has a wider range of effective bandwidth and higher signal-to-noise ratio than EEG. Moreover, differences in the spectral and spatiotemporal characteristics of MEG and EEG explain better performance. Simultaneous MEG-EEG recording experiments suggested that the hybrid MEG-EEG BCI achieved a significantly higher information transfer rate than either modality alone (hybrid: 312 ± 17 bits/min, MEG: 272 ± 17 bits/min, EEG: 240 ± 27 bits/min). Moreover, the 40-target classification accuracy of “BCI illiterate” increased from 50% to 95% with the help of MEG. These results highlight the methodological advantages of a hybrid MEG-EEG BCI, suggesting a promising paradigm for implementing high-speed BCIs.

Ballistocardiogram suppression in concurrent EEG-MRI by dynamic modeling of heartbeats.

The ballistocardiogram (BCG), the induced electric potentials by the head motion originating from heartbeats, is a prominent source of noise in electroencephalography (EEG) data during magnetic resonance imaging (MRI). Although methods have been proposed to suppress the BCG artifact, more work considering the variability of cardiac cycles and head motion across time and subjects is needed to provide highly robust correction. Here, a method called “dynamic modeling of heartbeats” (DMH) is proposed to reduce BCG artifacts in EEG data recorded inside an MRI system. The DMH method models BCG artifacts by combining EEG points at time instants with similar dynamics. The modeled BCG artifact is then subtracted from the EEG recording to suppress the BCG artifact. Performance of DMH was tested and specifically compared with the Optimal Basis Set (OBS) method on EEG data recorded inside a 3T MRI system with either no MRI acquisition (Inside‐MRI), echo‐planar imaging (EPI‐EEG), or fast MRI acquisition using simultaneous multi‐slice and inverse imaging methods (SMS‐InI‐EEG). In a steady‐state visual evoked response (SSVEP) paradigm, the 15‐Hz oscillatory neuronal activity at the visual cortex after DMH processing was about 130% of that achieved by OBS processing for Inside‐MRI, SMS‐InI‐EEG, and EPI‐EEG conditions. The DMH method is computationally efficient for suppressing BCG artifacts and in the future may help to improve the quality of EEG data recorded in high‐field MRI systems for neuroscientific and clinical applications.

Transferring Shared Responses Across Electrode Montages for Facilitating Calibration in High-Speed Brain Spellers.

Recent studies have shown that using the user's average steady-state visual evoked responses (SSVEPs) as the template to template-matching methods could significantly improve the accuracy and speed of the SSVEP-based brain- computer interface (BCI). However, collecting the pilot data for each individual can be time-consuming. To resolve this practical issue, this study aims to explore the feasibility of leveraging pre- recorded datasets from the same users by transferring common electroencephalogram (EEG) responses across different sessions with the same or different electrode montages. The proposed method employs spatial filtering techniques including response averaging, canonical correlation analysis (CCA), and task- related component analysis (TRCA) to project scalp EEG recordings onto a shared response domain. The transferability was evaluated by using 40-class SSVEPs recorded from eight subjects with nine electrodes on two different days. Three subsets of electrode montages were selected to simulate different scenarios such as identical, partly overlapped, and non-overlapped electrode placements across two sessions. The target identification accuracy of the proposed methods with transferred training data significantly outperformed a conventional training-free algorithm. The result suggests training data required in the BCI speller could be transferred from different EEG montages and/or headsets.

Interocular interaction of contrast and luminance signals in human primary visual cortex

Interocular interaction in the visual system occurs under dichoptic conditions when contrast and luminance are imbalanced between the eyes. Human psychophysical investigations suggest that interocular interaction can be explained by a contrast normalization model. However, the neural processes that underlie such interactions are still unresolved. We set out to assess, for the first time, the proposed normalization model of interocular contrast interactions using magnetoencephalography (MEG) and to extend this model to incorporate interactions based on interocular luminance differences. We used MEG to record steady-state visual evoked responses (SSVER), and functional magnetic resonance imaging (fMRI) to obtain individual retinotopic maps that we used in combination with MEG source imaging in healthy participants. Binary noise stimuli were presented in monocular or dichoptic viewing and were frequency-tagged at 4 and 6 Hz. The contrast of the stimuli was modulated in a range between 0 and 32%. Monocularly, we reduced the luminance by placing a 1.5 ND filter over one eye in the maximal contrast condition. This ND filter reduces the mean light level by a factor of 30 without any alteration to the physical contrast.We observed in visual area V1 a monotonic increase in the magnitude of SSVERs with changes in contrast from 0 to 32%. For both eyes, dichoptic masking induced a decrease in SSVER signal power. This power decrease was well explained by the normalization model. Reducing mean luminance delayed monocular processing by approximately 38 ms in V1. The reduced luminance also decreased the masking ability of the eye under the filter. Predictions based on a temporal filtering model for the interocular luminance difference prior to the model's binocular combination stage were incorporated to update the normalization model. Our results demonstrate that the signals resulting from different contrast or luminance stimulation of the two eyes are combined in a way that can be explained by an interocular normalization model.

Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency

In brain-computer interfaces (BCIs) that use the steady-state visual evoked response (SSVEP), the user selects a control command by directing attention overtly or covertly to one out of several flicker stimuli. The different control channels are encoded in the frequency, phase, or time domain of the flicker signals. Here, we present a new type of SSVEP BCI, which uses only a single flicker stimulus and yet affords controlling multiple channels. The approach rests on the observation that the relative position between the stimulus and the foci of overt attention result in distinct topographies of the SSVEP response on the scalp. By classifying these topographies, the computer can determine at which position the user is gazing. Offline data analysis in a study on 12 healthy volunteers revealed that 9 targets can be recognized with about 95±3% accuracy, corresponding to an information transfer rate (ITR) of 40.8 ± 3.3 b/min on average. We explored how the classification accuracy is affected by the number of control channels, the trial length, and the number of EEG channels. Our findings suggest that the EEG data from five channels over parieto-occipital brain areas are sufficient for reliably classifying the topographies and that there is a large potential to improve the ITR by optimizing the trial length. The robust performance and the simple stimulation setup suggest that this approach is a prime candidate for applications on desktop and tablet computers.

Toward a reliable PWM‐based light‐emitting diode visual stimulus for improved SSVEP response with minimal visual fatigue

Steady-state visual evoked response (SSVEP) is widely used in visual-based diagnosis and applications such as brain–computer interfacing due to its high information transfer rate and the capability to activate commands through simple gaze control. However, one major impediment in using flashing visual stimulus to obtain SSVEP is eye fatigue that prevents continued long-term use preventing practical deployment. This combined with the difficulty in establishing precise pulse-width modulation (PWM) that results in poorer accuracy warrants the development of appropriate approach to solve these issues. Various studies have suggested the usage of high frequencies of visual stimulus to reduce the visual fatigue for the user but this results in poor response performance. Here, the authors study the use of extremely high duty-cycles in the stimulus in the hope of solving these constraints. Electroencephalogram data was recorded with PWM duty-cycles of 50–95% generated by a precise custom-made light-emitting diode hardware and tested ten subjects responded that increasing duty-cycles had less visual strain for all the frequency values and the SSVEP exhibited a subject-independent peak response for duty-cycle of 85%. This could pave the way for increased usage of SSVEP for practical applications.

Dynamics of dichoptic masking in the primary visual cortex

The inputs from the two eyes interact in a nonlinear fashion. This interaction can be either excitatory or inhibitory: Excitatory interaction (combination) occurs first in the primary visual cortex but little is known about the site of the inhibitory interaction (suppression). To investigate the latter, experimental paradigms typically present distinct inputs to the eyes (dichoptic presentation, one target and one mask are respectively presented to different eyes at the same time). Here we used magnetoencephalography (MEG) source imaging to establish the site of the cortical neural signature of interocular suppression in visual cortex. We presented different noise stimuli to each eye: The target-noise was presented for contrasts ranging between 0 and 32 %. The mask-contrast was presented to the other eye at fixed contrast (32%). We flickered the two noise stimuli (4 and 6 Hz) to elicit a frequency-tagged steady-state visual evoked response (SSVER) that was readily detectable in MEG traces [1]. Four participants passively observed the visual presentation while keeping their gaze fixed on the center of the screen. The Brainstorm application was used to analyze the MEG data [3]. MEG source time series were extracted from cortical regions of interest (ROIs) defined from the visual retinotopic maps of each participant obtained from previous fMRI acquisitions. Using the power of the cortical responses to the frequency-tagged stimuli, we constructed contrast response functions for all the ROIs (Figure 1 A). To investigate dynamics of propagation of the response along visual cortical areas, the instantaneous phase of the signal was identified in each ROI. As expected [2], when the target was presented alone, the power of the responses was found to increase monotonically with contrast (Figure 1 B, solid line). When a mask was added to the other eye, the contrast response was attenuated (Figure 1 B, dotted line). Interestingly, the mask presented at a fixed contrast was also found to be gradually suppressed with increasing target contrast (Figure 1 C). These effects were revealed for responses as early as the primary visual cortex. In the time-domain, we detected a progressive phase shift between the cortical responses along the ventral and dorsal streams. Figure 1 A. Localization of V1 on the activity map B. Normalized power of SSVER response as a function of contrast for target only (solid line), target and mask (dotted line); C. Normalized power in a 32% contrast mask as the contrast in the target is increasing. ...

Steady-state visual-evoked response to upright and inverted geometrical faces: A magnetoencephalography study

The face is one of the most important visual stimuli in human life, and inverted faces are known to elicit different brain responses than upright faces. This study analyzed steady-state visual-evoked magnetic fields (SSVEFs) in eleven healthy participants when they viewed upright and inverted geometrical faces presented at 6Hz. Steady-state visual-evoked responses are useful measurements and have the advantages of robustness and a high signal-to-noise ratio. Spectrum analysis revealed clear responses to both upright and inverted faces at the fundamental stimulation frequency (6Hz) and harmonics, i.e. SSVEFs. No significant difference was observed in the SSVEF amplitude at 6Hz between upright and inverted faces, which was different from the transient visual-evoked response, N170. On the other hand, SSVEFs were delayed with the inverted face in the right temporal area, which was similar to N170 and the results of previous steady-state visual-evoked potentials studies. These results suggest that different mechanisms underlie the larger amplitude and delayed latency observed with face inversion, though further studies are needed to fully elucidate these mechanisms. Our study revealed that SSVEFs, which have practical advantages for measurements, could provide novel findings in human face processing.

Objective detection of Steady State Visual Evoked Responses for different lighting conditions

This work aims at evaluating the performance of two Objective Response Detection (ORD) techniques: the Magnitude-Squared Coherence (MSC) and the Multiple Coherence (MC) for detecting Steady State Visual Evoked Response (SSVEP) for two different lighting conditions (scotopic and photopic). EEG signals were collected (using a wireless headset) from ten volunteers without history of neurological diseases during stimulation with white LED flickering at frequencies of 6, 7, 8, 9, 10 and 11 Hz. The experimental protocol was composed by 13 sessions in a dark room and 13 in a bright one. The frequency order was randomized. The MSC was applied to derivations [P7], [P8], [O1] and [O2], and MC to the combinations of leads [O1-O2], [O1-O2-P7], [O1-O2-P8] and [O1-O2-P7-P8]. Both estimates were calculated using M = 11 epochs. The detection rates for MSC presented an important decrease at photopic condition compared to the scotopic one for all derivations and all frequencies. The employment of MC to [O1-O2-P7-P8] allowed overcoming this limitation, leading to detection percentages of at least 80% for all frequencies (except 9 Hz). These results show that MC is a promising technique for building SSVEP Bran Computer Interfaces (BCI).

Spatiotemporal property of event-related steady-state visual-evoked responses

Spatiotemporal property of event-related steady-state visual-evoked responses

Topography of Cortical Activation Differs for Fundamental and Harmonic Frequencies of the Steady-State Visual-Evoked Responses. An EEG and PET H215O Study

In humans, visual flicker stimuli of graded frequency (2-90 Hz) elicit an electroencephalographic (EEG) steady-state visual-evoked response (SSVER) with the same fundamental frequency as the stimulus and, in addition, a series of harmonic responses. The fundamental component of the SSVER is generated by increased synaptic activity in primary visual cortex (V1). We set out to determine the cortical origin of the harmonic responses in humans. For this purpose, we recorded the SSVERs at 5 different frequencies (5, 10, 15, 25, and 40 Hz) and measured regional cerebral blood flow (rCBF) with positron emission tomography-H(2)(15)O at rest and during visual stimulation at the same frequencies. The rCBF contrast weighted by the amplitude of the SSVERs first harmonics showed activation of a swath of cortex perpendicular to V1, including mostly the inferior half of the parieto-occipital sulcus. This area overlapped minimally with the primary visual cortex activated by the fundamental frequency. A different method, estimating EEG cortical source current density with low-resolution brain electromagnetic tomography, gave the same results. Our finding suggests that the inferior portion of the banks of the parieto-occipital sulci contains association visual cortex involved in the processing of stimuli that can be as simple as a flickering light source.

Human Cerebral Activation during Steady-State Visual-Evoked Responses

Flicker stimuli of variable frequency (2-90 Hz) elicit a steady-state visual-evoked response (SSVER) in the electroencephalogram (EEG) with the same frequency as the stimulus. In humans, the amplitude of this response peaks at approximately 15 Hz, decreasing at higher stimulation frequencies. It was not known whether this peak response corresponds to increased synaptic activity in the visual cortex or to other mechanisms [for instance, the temporal coherence (phase summation) of evoked responses]. We studied the SSVER in 16 normal volunteers by means of visual stimulation at 14 different frequencies (from 5 to 60 Hz) while recording the EEG. In nine subjects of the group, we measured regional cerebral blood flow (rCBF) with positron emission tomography (PET)-H2(15)O at rest and during visual stimulation at five different frequencies: 5, 10, 15, 25, and 40 Hz. We confirmed that the amplitude of the SSVER in occipital regions peaks at 15 Hz stimulation. Applying to the PET rCBF data a contrast weighted by the amplitude of the SSVER, we determined that the primary visual cortex rCBF follows an activation pattern similar to the SSVER. This finding suggests that the amplitude of the SSVER corresponds to increased synaptic activity, specifically in Brodmann's area 17. Additionally, this study showed that visual stimulation at 40 Hz causes selective activation of the macular region of the visual cortex, and that a region in the dorsal aspect of the Crus I lobule of the left cerebellar hemisphere is activated during repetitive visual stimulation.

Comparing mouse and steady-state visual evoked response-based control

Future computers will be more mobile, which will require new interaction methods. Accordingly, one might harness electroencephalographic (EEG) activity for computer control. Such devices exist, but all have limitations. Therefore, a novel EEG-based control was tested, which monitors the Steady-State Visual Evoked Response (SSVER). Selections are attempted by fixating a flickering target. A selection occurs if a SSVER is detected. To assess the device's relative performance, a mouse and the SSVER-based control were used to acquire targets of various sizes and distances. Accuracy and speed were measured. Overall, accuracy was poorer and acquisition times were longer with the SSVER-based control. However, the performance levels attained by the SSVER-based control might be adequate when manual controls are problematic, such as in assistive technology applications. In addition, in contrast to the mouse, SSVER-based acquisition times were insensitive to variations in target distance, which might serve as an operational advantage in certain applications.

Brain-computer interfaces based on the steady-state visual-evoked response

The Air Force Research Laboratory has implemented and evaluated two brain-computer interfaces (BCI's) that translate the steady-state visual evoked response into a control signal for operating a physical device or computer program. In one approach, operators self-regulate the brain response; the other approach uses multiple evoked responses.

The Effect of Task Difficulty on the Steady State Visual Evoked Response

Steady State Visual Evoked Responses to sum-of-sine wave modulated light were measured for subjects as they performed a decision making task. Describing function phase values demonstrated a positive shift under task conditions. For those subjects classified as Alpha producers (peak response in the 8 to 12 Hz region) there was an attenuation of gain with increasing task difficulty. In the beta range (above 12 Hz) a resistance to gain attenuation with increasing task difficulty, even to the point of gain increase, was observed.

Steady-State Visual Evoked Response Amplitudes and Concurrent Electroencephalographic Activity

Among the possible causes of steady-state visual evoked response (VER) amplitude variability are: concurrent electroencephalographic (EEG) activity occurring at frequencies other than that of the VER (alpha rhythms, etc.), and EEG activity occurring at the same frequency as the VER (Noise). To evaluate these two factors, 10 steady-state VER's (7.8 Hz pattern reversal rate) and 10 samples of resting EEG activity were obtained from each of 20 normal subjects. The correlations between VER and concurrent EEG activity at other frequencies were calculated from simultaneous determinations of Fourier-derived VER and EEG amplitudes. No significant correlations were found. Because EEG Noise occurring at the same frequency and time as the VER cannot easily be separated from the VER by Fourier transformation, two indirect techniques for assessing the amplitude of this Noise were evaluated: measurement of simultaneously recorded activity at frequencies directly adjacent to the 15.6 Hz VER frequency and measurement of EEG activity at 15.6 Hz obtained just before the VER measurements. Neither of these procedures could predict trial-by-trial variations in the amplitude of the Noise at 15.6 Hz. However, across subjects both procedures provided good estimates of the mean EEG Noise underlying the VER.

Contributions of Luminosity and Color Contrast to the Steady State Evoked Response

The relation between stimulus colors and the steady state visual-evoked response was studied. The responses were evoked by 6.8 Hz interrupted colored beams. Simultaneous adaptation occurred when stead