Kunming University of Science and Technology

Researchers from Kunming University of Science and Technology and collaborating institutions proposed a theoretical system for basic BCI research, formalizing BCIs as closed-loop probabilistic information-processing models through information theory and statistical decision-making. The work addresses system decodability, performance boundaries and dynamic constraints. It organizes scientific hypotheses, neuroscience principles, fundamental theorems and basic laws, derives four results that can guide experimental design, and uses motor imagery BCIs to illustrate application boundaries. The authors aim to provide a unified analytical benchmark and support a shift from experience-driven algorithm optimization toward theory-driven system analysis and design.

Researchers at Kunming University of Science and Technology reviewed the mechanisms and outcome evaluation of neurofeedback for post-traumatic stress disorder. They compared EEG, real-time functional magnetic resonance imaging and functional near-infrared spectroscopy in feedback targets, spatiotemporal resolution and clinical applicability. Neurofeedback provides real-time information about brain activity to guide patients in regulating abnormal neural patterns. Using clinical scales, neurophysiological measures and functional recovery indicators, the review discusses its clinical position relative to first-line treatments and potential synergy with psychological interventions. The authors identify inadequate control designs and non-standardized training parameters as ongoing limitations; therapeutic potential should not be treated as established efficacy.

Brain-computer interfaces (BCIs) face human factors bottlenecks such as reliability and usability as they move toward real-world use, yet human factors engineering has mostly studied overt behavior and peripheral physiology rather than brain activity as a control input. This conceptual review proposes Human Factors Engineering for BCIs as an interdisciplinary direction, centered on user neurocognitive traits, brain-computer co-adaptation, multimodal feedback and context, with 3 core scientific questions and ethics, privacy and user rights as an overarching dimension.

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 team at Kunming University of Science and Technology, in southwestern China's Yunnan province, has published a paper in the Journal of Biomedical Engineering analyzing how inherent limitations set the capability boundaries of brain-computer interfaces (BCIs). Dynamic neural coding, inter-individual variability, low signal-to-noise ratio, partial observability and paradigm dependence jointly impose upper limits on decoding accuracy, information transfer rate, complex intention decoding, user experience and system stability. The authors propose information enhancement, adaptive decoding, human-machine collaboration and system optimization, arguing that gains will come from extracting more from the neural signal rather than from overcoming the underlying limits.

DRDNet separates spatial EEG features into two temporal views, models them with a bidirectional Mamba encoder and a Transformer, and then combines them through dynamic fusion and LSTM aggregation. On a public dataset, it reached 67.74% accuracy for imagined Chinese-character strokes and 62.51% for imagined pinyin vowels, outperforming seven EEG-decoding baselines.