Beijing Institute of Technology

BCI hardware and decoding algorithms have advanced quickly, but basic questions such as how much intent can be read from brain signals and how low decoding error can go still lack a systematic theoretical answer. This paper treats a BCI as a closed-loop stochastic process: a user forms an intention, the brain encodes it as neural activity, the device observes the signal, an algorithm decodes it statistically, and feedback returns to the user. Using information theory and statistical decision theory, the authors derive theorems on when intent is decodable, the upper bound of observable information, the lower bound of optimal decoding error, and the convergence of closed-loop learning, plus laws describing constraints from signal-to-noise ratio, class separability and non-stationary co-adaptation. A four-class SSVEP-BCI, in which users gaze at targets flickering at different frequencies, serves as a theoretical case study. The work is theoretical and reports no new experimental data.

A preprint proposes an adaptive EEG generation framework based on conditional flow matching to ease data scarcity in brain-computer interfaces and support large-scale neural modeling. Noting that existing flow methods assume one global time course across all channels and time segments, the framework adds position-adaptive time scheduling that tracks per-position reconstruction error to modulate each position's time course, plus decomposed spatiotemporal attention and a frequency-aligned multi-resolution spectral consistency loss to model cross-channel dependencies and compensate for EEG's power-law spectral bias. Across three EEG datasets with different acquisition protocols and task semantics, it consistently beat the strongest baselines, cutting TS-FID by up to 62.2% and lifting downstream classification accuracy by up to 6.77 percentage points. The study has not been peer reviewed.