Invasive brain-computer interfaces have garnered significant attention due to
their high performance. The current intracranial stereoElectroEncephaloGraphy
(sEEG) foundation models typically build univariate representations based on a
single channel. Some of them further use Transformer to model the relationship
among channels. However, due to the locality and specificity of brain
computation, their performance on more difficult tasks, e.g., speech decoding,
which demands intricate processing in specific brain regions, is yet to be
fully investigated. We hypothesize that building multi-variate representations
within certain brain regions can better capture the specific neural processing.
To explore this hypothesis, we collect a well-annotated Chinese word-reading
sEEG dataset, targeting language-related brain networks, over 12 subjects.
Leveraging this benchmark dataset, we developed the Du-IN model that can
extract contextual embeddings from specific brain regions through discrete
codebook-guided mask modeling. Our model achieves SOTA performance on the
downstream 61-word classification task, surpassing all baseline models. Model
comparison and ablation analysis reveal that our design choices, including (i)
multi-variate representation by fusing channels in vSMC and STG regions and
(ii) self-supervision by discrete codebook-guided mask modeling, significantly
contribute to these performances. Collectively, our approach, inspired by
neuroscience findings, capitalizing on multi-variate neural representation from
specific brain regions, is suitable for invasive brain modeling. It marks a
promising neuro-inspired AI approach in BCI.

通过构建多元神经表示，Du-IN 模型可以从特定的脑区提取上下文嵌入，从而在语言相关的脑网络上实现了 SOTA 性能。