PyraNet: A Lightweight Neural Decoder for Diverse BCI Paradigms

Neural signal decoding relies on learning effective representations of brain activity. Existing models have largely followed two paths: compact decoders and large pretrained foundation models. The former perform well on certain target paradigms but struggle on others. The latter seek broad generalizability through increased capacity and cross-dataset pretraining but often fail to deliver performance commensurate with their size. This raises a fundamental question: Must general-purpose neural decoding rely on scaling up model size, or can broad applicability be achieved with a lightweight architecture informed by domain knowledge? To address this question, we propose PyraNet, a lightweight, neurophysiology-informed architecture. Specifically, we devise an encoder in which learned spatial filters capture discriminative cross-channel patterns, while grouped and dilated convolutions integrate these patterns over multiple temporal receptive fields to model coupled spatiotemporal dependencies within a small parameter budget. We further introduce a dataset-level adaptive grid selector that automatically configures feature aggregation using the same penalty factors across datasets, without requiring manual granularity selection. After a shared warm-up, it selects the grid with the lowest penalized validation loss and retains it for subsequent training and inference. We evaluated PyraNet against six compact models and three advanced foundation models across eleven electroencephalography (EEG), magnetoencephalography (MEG), and electrocorticography (ECoG) datasets spanning five decoding paradigms: motor imagery (MI), P300 detection, steady-state visual evoked potential (SSVEP) decoding, mental arithmetic (MA) state classification, and speech decoding. PyraNet demonstrated consistently strong performance across all eleven datasets, with the best average rank of 1.8 and a mean balanced accuracy (BAcc) of 66.93%, exceeding the runner-up by 3.1 percentage points. On the nine EEG datasets shared with the foundation models, PyraNet achieved a mean BAcc of 77.55%, outperforming the strongest foundation model by 3.1 percentage points, with approximately one-thousandth as many parameters.