iMINDBench: iEEG Multi-Institution Neural Decoding Benchmark

Intracranial electroencephalography (iEEG) records electrical activity directly from the human brain, making it attractive for neural decoding. However, progress toward general-purpose iEEG foundation models is difficult to measure because fragmented datasets and processing pipelines obscure whether model gains persist across institutions, arise from modeling rather than preprocessing, or grow with broader supervision. Thus, we introduce iMINDBench, an iEEG Multi-Institution Neural Decoding Benchmark that aligns fifteen naturalistic decoding tasks across three independently collected institutional datasets to measure progress across heterogeneous recordings. We define standardized spectral and waveform preprocessing tracks and find that pretrained systems generally outperform non-pretrained baselines within their respective tracks, yet an engineered spectral baseline remains competitive with pretrained systems, especially on the two datasets with less downstream training data. The benchmark's harmonized targets and electrode metadata also enable a matched-task scaling study across subjects and institutions. We find that additional same-dataset subjects yield modest gains, whereas adding data from other institutions provides little aggregate benefit despite up to a 25-fold expansion of the supervised dataset. These findings highlight the need for general-purpose iEEG decoders that deliver consistent gains across institutions and make more effective use of cross-institution supervision.