The graph theoretical analysis of the SSVEP harmonic response networks

Abstract

Steady-state visually evoked potentials (SSVEP) have been widely used in the neural engineering and cognitive neuroscience researches. Previous studies have indicated that the SSVEP fundamental frequency responses are correlated with the topological properties of the functional networks entrained by the periodic stimuli. Given the different spatial and functional roles of the fundamental frequency and harmonic responses, in this study we further investigated the relation between the harmonic responses and the corresponding functional networks, using the graph theoretical analysis. We found that the second harmonic responses were positively correlated to the mean functional connectivity, clustering coefficient, and global and local efficiencies, while negatively correlated with the characteristic path lengths of the corresponding networks. In addition, similar pattern occurred with the lowest stimulus frequency (6.25 Hz) at the third harmonic responses. These findings demonstrate that more efficient brain networks are related to larger SSVEP responses. Furthermore, we showed that the main connection pattern of the SSVEP harmonic response networks originates from the interactions between the frontal and parietal-occipital regions. Overall, this study may bring new insights into the understanding of the brain mechanisms underlying SSVEP.

Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges

“…In our study (Ordikhani-Seyedlar et al, 2014 ) that employed a covert attention paradigm, the power of the second harmonic was higher compared to the first harmonic. This result is in agreement with Kim et al ( 2011 ) and others Garcia et al ( 2013 ), Zhang et al ( 2015 ) who also reported that visual spatial attention modulates the second, but not the first harmonic of the SSVEP frequency.…”

Steady state visual evoked potentials in reading aloud: Effects of lexicality, frequency and orthographic familiarity

“…No modulation of SSVEP power was obtained for the orthographic familiarity effect (familiar versus unfamiliar pseudowords), which suggests that SSVEP taps lexical rather than sublexical mechanisms. Overall, our results are compatible with the hypothesis that the SSVEP power reflects the underlying network efficiency (Zhang et al, 2015) because words benefit from more efficient lexical network dynamics than pseudowords, which also results in shorter reaction times and higher accuracy in standard naming tasks. Similarly, high-frequency words also benefit from more efficient lexical networks because they tend to have stronger network connections or resting levels, which typically results in faster RTs and higher accuracy for high-frequency over low-frequency words.…”

“…Although the SSVEP response modulation could arise from different processes (Bergholz, Lehmann, Fritz, & Ruther, 2008;Rossion, & Boremanse, 2011), it is generally assumed that larger SSVEP responses are caused by more efficient network dynamics (Zhang, Guo, Cheng, Yao, & Xu, 2015). For example, in a visual search task, SSVEP amplitude in the feature-conjunction condition reflected increased salience and rapid localization of feature-conjunction targets (Andersen, Hillyard, & Müller, 2008).…”

Improved SFFS method for channel selection in motor imagery based BCI

“…Various neural activities can be used as features in electroencephalogram (EEG)-based BCI systems. P300 evoked potentials [5][6][7][8][9][10], slow cortical potentials, steady state visually evoked potentials [11,12], and event-related desynchronization (ERD) [ [14] are extensively used in BCI systems.…”