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Researchers at Bishop’s University compared visual decoding using EEG, MEG, intracranial EEG, and fMRI. Individual differences were reproducible within an instrument, but external paired cohorts did not show a common participant ranking across instruments. Classifiers were tested on recording runs and stimulus identities excluded from training. Attention, behavior, and peripheral signals affected recording quality but did not explain most stable differences. Intracranial recordings also linked sharper local category selectivity to easier decoding. The authors interpret these differences as reflecting the relationship between the individual, the distinction being represented, and the instrument, rather than a fixed personal trait alone.

A collaboration involving Peking University converted dynamic natural movie frames into spatiotemporal patterns of optogenetic stimulation in awake mice, then reconstructed movie frames from responses in the primary visual cortex. Comparing natural viewing with artificial stimulation, they found that stimulation evoked broadly comparable cortical dynamics and aligned representations. A deep learning model reconstructed frames from cortical signals under both conditions. The animal study offers a proof of concept for evaluating how visual cortical prostheses might translate images into stimulation patterns. It does not establish that stimulation produced the corresponding visual experience in mice or restored vision in humans.

An offline EEG study of 15 participants found that on-screen flickering targets stepping up or down in frequency can be recognized as brain-control commands, but they did not classify more accurately than conventional fixed-frequency flicker. Mean normalized accuracies were 89.7% for fixed frequency, 85.6% for ascending and 89.5% for descending patterns, a statistically insignificant difference. Low-frequency stimulation at 6–10 Hz reached 90.8%, versus 80.6% at 41–45 Hz. Sequences of three consecutive commands averaged 91.5 ± 9.6%, with accuracy falling as sequences grew longer. The researchers see the main value in more flexible design of consecutive commands and say larger samples and online closed-loop testing are needed.

The First Affiliated Hospital of Bengbu Medical University, in eastern China's Anhui province, is recruiting for a clinical trial that uses brain-computer interface (BCI) neurofeedback to treat the cognitive-control deficits that keep people with internet gaming disorder from stopping play. The system reads frontal midline theta rhythms and feeds them back in real time so participants learn to regulate their own brain activity, with social rewards built in as reinforcers to improve efficacy and adherence. The trial plans to enroll 120 patients across three arms: dual-target neurofeedback plus social reward, neurofeedback alone, and sham feedback. Primary completion is expected on December 30, 2028.

Researchers have developed an adaptive version of Artifact Subspace Reconstruction (ASR) that removed 53.1% of blink artifacts from EEG recordings, compared with 24.0% for the original ASR and 26.7% for a limited-memory variant. EEG signals drift over time and are often contaminated by blinks and other artifacts, a problem BCIs must solve for long-term use in real-world settings. Standard ASR suppresses artifacts automatically based on variance but relies on a static covariance model derived from calibration data, which becomes less robust as signals drift. The new method tracks signal structure with an exponential moving average, combining short-term covariance estimates with slow recursive updates so the model adapts on a fast and a slow timescale at once. It was tested on 24-channel EEG from 10 subjects across different cognitive states. The trade-off was larger reconstruction and spectral deviation. The study is a preprint and has not been peer reviewed.

Researchers testing 3 EEG foundation models, general-purpose models pretrained on large amounts of EEG data, on 235 subjects excluded from training found that a lightweight adapter trained for each individual raised mean balanced accuracy by 1.5 to 5.4 percentage points over the shared population model. Every new brain-computer interface user needs calibration, and a personally tuned model may be more accurate either because it captures the individual's traits or simply because the underlying general model is stronger. Personal adapters also beat adapters borrowed from other people by 2.3 to 7.3 points. When the population model's training was scaled up fourfold, the personalization gain stayed positive but narrowed. Low-cost calibration using only a few labeled samples gave inconsistent results. The study is a preprint and has not been peer reviewed.

Researchers have proposed BrainVLA, a framework that borrows a vision-language-action model pretrained on large amounts of robot data to decode what a paralyzed user intends to do. A key obstacle for BCIs that control external devices is the scarcity of paired neural and movement data; the usual approach of learning a task-specific map from neural activity to kinematic parameters is held back by that shortage. The team first built a paired dataset of neural activity, action signals, language instructions and rendered visual observations, then adapted the OpenVLA-OFT policy to the target action space through LoRA fine-tuning. Neural activity is aligned with language representations to extract an intention signal, which the model combines with visual input to generate executable actions. On 2 neural motor datasets with different action dimensions, the framework outperformed the evaluated baselines in cross-session decoding R² and task success rate, and was data-efficient to train. The study is a preprint and has not been peer reviewed.

Researchers have built AutoBCI, a framework in which AI agents design EEG decoding models, and report that it slightly outperformed the strongest existing model across 14 datasets. Building a decoder that works well across many EEG tasks usually takes repeated trial and error. AutoBCI splits the job between 2 agents: one generates and refines architectures, while the other forecasts final performance from early learning curves to pick out candidates worth training further. Across 14 EEG datasets covering motor imagery, emotion recognition and sleep staging, the architecture found with Claude Opus 5.5 reached an average test balanced accuracy of 64.16%, slightly above the 63.87% of REVE, the strongest baseline. Looking at only 10 training epochs, the forecasting agent cut prediction error from 2.20 to 1.36 percentage points. The study is a preprint and has not been peer reviewed.