The relationship between ERP components and EEG spatial complexity in a visual Go/Nogo task

The ERP components and variations of spatial complexity or functional connectivity are two distinct dimensions of neurophysiological events in the visual Go/Nogo task. Extensive studies have been conducted on these two distinct dimensions; however, no study has investigated whether these two neurophysiological events are linked to each other in the visual Go/Nogo task. The relationship between spatial complexity of electroencephalographic (EEG) data, quantified by the measure omega complexity, and event-related potential (ERP) components in a visual Go/Nogo task was studied. We found that with the increase of spatial complexity level, the latencies of N1 and N2 component were shortened and the amplitudes of N1, N2, and P3 components were decreased. The anterior Go/Nogo N2 effect and the Go/Nogo P3 effect were also found to be decreased with the increase of EEG spatial complexity. In addition, the reaction times in high spatial complexity trials were significantly shorter than those of medium and low spatial complexity trials when the time interval used to estimate the EEG spatial complexity was extended to 0~1,000 ms after stimulus onset. These results suggest that high spatial complexity may be associated with faster cognitive processing and smaller postsynaptic potentials that occur simultaneously in large numbers of cortical pyramidal cells of certain brain regions. The EEG spatial complexity is closely related with demands of certain cognitive processes and the neural processing efficiency of human brain.

NEW & NOTEWORTHY The reaction times, the latencies/amplitudes of event-related potential (ERP) components, the Go/Nogo N2 effect, and the Go/Nogo P3 effect are linked to the electroencephalographic (EEG) spatial complexity level. The EEG spatial complexity is closely related to demands of certain cognitive processes and could reflect the neural processing efficiency of human brain. Obtaining the single-trial ERP features through single-trial spatial complexity may be a more efficient approach than traditional methods.

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