Abstract
Despite decades of electroencephalography (EEG) research, the relationship between EEG and underlying spiking dynamics remains unclear. This limits our ability to infer intracranial signals from EEG, a critical step to bridge electrophysiological findings across species and to develop non-invasive brain-machine interfaces (BMIs). We recorded spiking activity from a 32-channel floating microarray permanently implanted in parafoveal V1 and scalp-EEG in a male macaque monkey. While the animal fixated, the screen flickered at different temporal frequencies (0, 5, 10, 20, and 40 Hz) to induce steady-state visual evoked potentials (SSVEP). The primary advantage of SSVEPs is that they generate high signal-to-noise ratios. We analyzed the relationship between the SSVEPs in multiunit spiking activity (MUA) and EEG. Both MUA and EEG showed robust SSVEPs, with best response in EEG for 20Hz-stimulus. The MUA also showed strong responses at the harmonics of the stimulus frequencies, which was not evident in EEG. Time-series correlation between trial-averaged EEG and MUA showed strongest relationship for 5Hz- and 10Hz-stimuli. Furthermore, correlating MUA with EEG power at different frequencies (1-200 Hz) showed prominent correlations for 5Hz-stimulus, which was limited to specific EEG bands (5-10, 10-20, and 40-70 Hz). This correlation pattern was consistent across intracranial electrodes placed at different depths in V1, suggesting that the 5Hz stimulus is optimal for estimating spiking activity from EEG. Single-trial EEG-MUA correlations lacked stimulus-specific relationships. However, a 10 ms delay in EEG signal yielded consistent negative correlations with spiking activity across intracranial electrode depths. This suggests that delayed EEG signals may reflect information about the spiking activity and could be used to estimate MUA from EEG. Our study shows robust relationships between V1 spiking activity and EEG under frequency-specific stimulus conditions. These results give direction to better estimate cortical spiking activity using non-invasive scalp EEG.