Robust Neural Decoding with low-density EEG

Abstract High-density Electroencephalography (EEG) recording enhances spatial resolution for neural signal decoding, yet the relationship between electrode density and decoding performance remains unclear. To address this, we systematically investigated decoding accuracy across electrode configurations of varying densities (16, 32, 64, 96, and 128 electrodes) using visual grating stimuli characterized by orientation, contrast, spatial frequency, and color. As expected, decoding accuracy increased with electrode density. Remarkably, however, reliable above-chance decoding was still achieved with as few as 16 electrodes, highlighting the robustness of decodable neural signals. To test the generalization of these results to more complex natural stimuli, we conducted a similar analysis with a diverse set of naturalistic images categorizable into living/non-living and moving/non-moving. The results consistently showed that effective decoding persists even with a 16-electrode configuration, showing robust decoding efficacy even for complex naturalistic stimuli. These findings demonstrate both the benefits of higher-density EEG and the robustness of neural decoding under sparse spatial sampling, providing new insights into how efficiently and broadly neural signals can be decoded.