SSVEP phase synchronies and propagation during repetitive visual stimulation at high frequencies

Single-trial EEG analysis reveals burst structure during photic driving

OBJECTIVE

Photic driving in the human visual cortex evoked by intermittent photic stimulation is usually characterized in averaged data by an ongoing oscillation showing frequency entrainment and resonance phenomena during the course of stimulation. We challenge this view of an ongoing oscillation by analyzing unaveraged data.

METHODS

64-channel EEGs were recorded during visual stimulation with light flashes at eight stimulation frequencies between 7.8 and 23 Hz for fourteen healthy volunteers. Time-frequency analyses were performed in averaged and unaveraged data.

RESULTS

While we find ongoing oscillations in the averaged data during intermittent photic stimulation, we find transient events (bursts) of activity in the unaveraged data. Both resonance and entrainment occur for the ongoing oscillations in the averaged data and the bursts in the unaveraged data.

CONCLUSIONS

We argue that the continuous oscillations in the averaged signal may be composed of brief, transient bursts in single trials. Our results can also explain previously observed amplitude fluctuations in averaged photic driving data.

SIGNIFICANCE

Single-trial analyses might consequently improve our understanding of resonance and entrainment phenomena in the brain.

Electrophysiological model of human temporal contrast sensitivity based on SSVEP

The present study aims to connect the psychophysical research on the human visual perception of flicker with the neurophysiological research on steady-state visual evoked potentials (SSVEPs) in the context of their application needs and current technological developments. In four experiments, we investigated whether a temporal contrast sensitivity model could be established based on the electrophysiological responses to repetitive visual stimulation and, if so, how this model compares to the psychophysical models of flicker visibility. We used data from 62 observers viewing periodic flicker at a range of frequencies and modulation depths sampled around the perceptual visibility thresholds. The resulting temporal contrast sensitivity curve (TCSC) was similar in shape to its psychophysical counterpart, confirming that the human visual system is most sensitive to repetitive visual stimulation at frequencies between 10 and 20 Hz. The electrophysiological TCSC, however, was below the psychophysical TCSC measured in our experiments for lower frequencies (1-50 Hz), crossed it when the frequency was 50 Hz, and stayed above while decreasing at a slower rate for frequencies in the gamma range (40-60 Hz). This finding provides evidence that SSVEPs could be measured even without the conscious perception of flicker, particularly at frequencies above 50 Hz. The cortical and perceptual mechanisms that apply at higher temporal frequencies, however, do not seem to directly translate to lower frequencies. The presence of harmonics, which show better response for many frequencies, suggests non-linear processing in the visual system. These findings are important for the potential applications of SSVEPs in studying, assisting, or augmenting human cognitive and sensorimotor functions.

Steady state visual evoked potentials reveal a signature of the pitch-size crossmodal association in visual cortex

Crossmodal correspondences describe our tendency to associate sensory features from different modalities with each other, such as the pitch of a sound with the size of a visual object. While such crossmodal correspondences (or associations) are described in many behavioural studies their neurophysiological correlates remain unclear. Under the current working model of multisensory perception both a low- and a high-level account seem plausible. That is, the neurophysiological processes shaping these associations could commence in low-level sensory regions, or may predominantly emerge in high-level association regions of semantic and object identification networks. We exploited steady-state visual evoked potentials (SSVEP) to directly probe this question, focusing on the associations between pitch and the visual features of size, hue or chromatic saturation. We found that SSVEPs over occipital regions are sensitive to the congruency between pitch and size, and a source analysis pointed to an origin around primary visual cortices. We speculate that this signature of the pitch-size association in low-level visual cortices reflects the successful pairing of congruent visual and acoustic object properties and may contribute to establishing causal relations between multisensory objects. Besides this, our study also provides a paradigm can be exploited to study other crossmodal associations involving visual stimuli in the future.

EEGformer: A transformer-based brain activity classification method using EEG signal

Background

The effective analysis methods for steady-state visual evoked potential (SSVEP) signals are critical in supporting an early diagnosis of glaucoma. Most efforts focused on adopting existing techniques to the SSVEPs-based brain-computer interface (BCI) task rather than proposing new ones specifically suited to the domain.

Method

Given that electroencephalogram (EEG) signals possess temporal, regional, and synchronous characteristics of brain activity, we proposed a transformer-based EEG analysis model known as EEGformer to capture the EEG characteristics in a unified manner. We adopted a one-dimensional convolution neural network (1DCNN) to automatically extract EEG-channel-wise features. The output was fed into the EEGformer, which is sequentially constructed using three components: regional, synchronous, and temporal transformers. In addition to using a large benchmark database (BETA) toward SSVEP-BCI application to validate model performance, we compared the EEGformer to current state-of-the-art deep learning models using two EEG datasets, which are obtained from our previous study: SJTU emotion EEG dataset (SEED) and a depressive EEG database (DepEEG).

Results

The experimental results show that the EEGformer achieves the best classification performance across the three EEG datasets, indicating that the rationality of our model architecture and learning EEG characteristics in a unified manner can improve model classification performance.

Conclusion

EEGformer generalizes well to different EEG datasets, demonstrating our approach can be potentially suitable for providing accurate brain activity classification and being used in different application scenarios, such as SSVEP-based early glaucoma diagnosis, emotion recognition and depression discrimination.

Disturbances in primary visual processing as a function of healthy aging

For decades, visual entrainment paradigms have been widely used to investigate basic visual processing in healthy individuals and those with neurological disorders. While healthy aging is known to be associated with alterations in visual processing, whether this extends to visual entrainment responses and the precise cortical regions involved is not fully understood. Such knowledge is imperative given the recent surge in interest surrounding the use of flicker stimulation and entrainment in the context of identifying and treating Alzheimer's disease (AD). In the current study, we examined visual entrainment in eighty healthy aging adults using magnetoencephalography (MEG) and a 15 Hz entrainment paradigm, while controlling for age-related cortical thinning. MEG data were imaged using a time-frequency resolved beamformer and peak voxel time series were extracted to quantify the oscillatory dynamics underlying the processing of the visual flicker stimuli. We found that, as age increased, the mean amplitude of entrainment responses decreased and the latency of these responses increased. However, there was no effect of age on the trial-to-trial consistency in phase (i.e., inter-trial phase locking) nor amplitude (i.e., coefficient of variation) of these visual responses. Importantly, we discovered that the relationship between age and response amplitude was fully mediated by the latency of visual processing. These results indicate that aging is associated with robust changes in the latency and amplitude of visual entrainment responses within regions surrounding the calcarine fissure, which should be considered in studies examining neurological disorders such as AD and other conditions associated with increased age.

Optimal flickering light stimulation for entraining gamma rhythms in older adults

With aging, optimal parameters of flickering light stimulation (FLS) for gamma entrainment may change in the eyes and brain. We investigated the optimal FLS parameters for gamma entrainment in 35 cognitively normal old adults by comparing event-related synchronization (ERS) and spectral Granger causality (sGC) of entrained gamma rhythms between different luminance intensities, colors, and flickering frequencies of FLSs. ERS entrained by 700 cd/m² FLS and 32 Hz or 34 Hz FLSs was stronger than that entrained by 400 cd/m² at Pz (p < 0.01) and 38 Hz or 40 Hz FLSs, respectively, at both Pz (p < 0.05) and Fz (p < 0.01). Parieto-occipital-to-frontotemporal connectivities of gamma rhythm entrained by 700 cd/m² FLS and 32 Hz or 34 Hz FLSs were also stronger than those entrained by 400 cd/m² at Pz (p < 0.01) and 38 Hz or 40 Hz FLSs, respectively (p < 0.001). ERS and parieto-occipital-to-frontotemporal connectivities of entrained gamma rhythms did not show significant difference between white and red lights. Adverse effects were comparable between different parameters. In older adults, 700 cd/m² FLS at 32 Hz or 34 Hz can entrain a strong gamma rhythm in the whole brain with tolerable adverse effects.

α Phase-Amplitude Tradeoffs Predict Visual Perception

Spontaneous α oscillations (∼10 Hz) have been associated with various cognitive functions, including perception. Their phase and amplitude independently predict cortical excitability and subsequent perceptual performance. However, the causal role of α phase-amplitude tradeoffs on visual perception remains ill-defined. We aimed to fill this gap and tested two clear predictions from the pulsed inhibition theory according to which α oscillations are associated with periodic functional inhibition. (1) High-α amplitude induces cortical inhibition at specific phases, associated with low perceptual performance, while at opposite phases, inhibition decreases (potentially increasing excitation) and perceptual performance increases. (2) Low-α amplitude is less susceptible to these phasic (periodic) pulses of inhibition, leading to overall higher perceptual performance. Here, cortical excitability was assessed in humans using phosphene (illusory) perception induced by single pulses of transcranial magnetic stimulation (TMS) applied over visual cortex at perceptual threshold, and its postpulse evoked activity recorded with simultaneous electroencephalography (EEG). We observed that prepulse α phase modulates the probability to perceive a phosphene, predominantly for high-α amplitude, with a nonoptimal phase for phosphene perception between -π/2 and -π/4. The prepulse nonoptimal phase further leads to an increase in postpulse-evoked activity [event-related potential (ERP)], in phosphene-perceived trials specifically. Together, these results show that α oscillations create periodic inhibitory moments when α amplitude is high, leading to periodic decrease of perceptual performance. This study provides strong causal evidence in favor of the pulsed inhibition theory.

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate-ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches-the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey's test.