Recent brain decoding studies have primarily emphasized the development of brain decoders, while largely neglecting the segmentation step. Existing methods typically adopt fixed-length segmentation, which might overlook subject- or task-level variability and disrupt intrinsic neural structures within brain signals. To address this gap, we propose $\textbf{S}^\textbf{3}$, which leverages spiking neurons as an isolating segmenter for brain signal decoding. $\textbf{S}^\textbf{3}$ segments brain signals adaptively, considering subject- and task-level variability while preserving intrinsic neural structures in brain signals. It exploits the unique reset mechanism of spiking neurons to enforce temporal pattern isolation for the generation of each segmentation point. To optimize $\textbf{S}^\textbf{3}$ for enhancing task performance in the absence of segmentation labels, we develop an optimization method where pseudo-labels are created with a stochastic-greedy algorithm to optimize them, circumventing gradient blockade between them. Experiments on 10 downstream tasks across 13 public datasets demonstrate that $\textbf{S}^\textbf{3}$ consistently outperforms existing methods, validating its effectiveness, generalizability and interpretability.