Functional near-infrared spectroscopy can detect low-frequency hemodynamic oscillations in the prefrontal cortex during steady-state visual evoked potentialinducing periodic facial expression stimuli presentation

Brain oscillations are vital to cognitive functions,while disrupted oscillatory activity is linked to various brain disorders.Although high-frequency neural oscillations(>1 Hz)have been extensively studied in cognition,the neural mechanisms underlying low-frequency hemodynamic oscillations(LFHO)<1 Hz have not yet been fully explored.One way to examine oscillatory neural dynamics is to use a facial expression(FE)paradigm to induce steady-state visual evoked potentials(SSVEPs),which has been used in electroencephalography studies of high-frequency brain oscillation activity.In this study,LFHO during SSVEP-inducing periodic flickering stimuli presentation were inspected using functional near-infrared spectroscopy(fNIRS),in which hemodynamic responses in the prefrontal cortex were recorded while participants were passively viewing dynamic FEs flickering at 0.2 Hz.The fast Fourier analysis results demonstrated that the power exhibited monochronic peaks at 0.2 Hz across all channels,indicating that the periodic events successfully elicited LFHO in the prefrontal cortex.More importantly,measurement of LFHO can effectively distinguish the brain activation difference between different cognitive conditions,with happy FE presentation showing greater LFHO power than neutral FE presentation.These results demonstrate that stimuli flashing at a given frequency can induce LFHO in the prefrontal cortex,which provides new insights into the cognitive mechanisms involved in slow oscillation.

A Method of SSVEP Signal Identification Based on Improved eCAA

Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.