Papers

Running a motor-imagery BCI while transcranial alternating current stimulation (tACS) is switched on has been difficult, because stimulation artifacts swamp the brain rhythms the decoder relies on and push accuracy close to chance. In a controlled test with 14 healthy participants, a real-time spatial filtering pipeline combining spatio-spectral decomposition with beamforming held accuracy at 76 ± 9% during stimulation, while a standard Laplacian filter managed only 58 ± 9%. That makes closed-loop "stimulate while reading" operation feasible at the signal level; whether it actually improves BCI performance or produces neuroplastic changes, the paper explicitly leaves to future research.

The study was published in Journal of neural engineering on August 13, 2026, presenting the first strength-duration characterization of cortical evoked potentials elicited by endovascular stimulation adjacent to the cerebellum. The authors note that electrical stimulation and neural recording underpin neural prostheses for restoring function and treating neurological disorders, but that clinical adoption is limited by the invasiveness of implantation, while the Endovascular Neural Interface offers an alternative by accessing intracranial targets through the cerebral vasculature. A polymer-based stent-electrode array was deployed into the left transverse sinus of an ovine model, and biphasic current pulses targeting the cerebellum were delivered via the stent electrodes while a subdural electrocorticography grid recorded cortical responses. Endovascular stimulation consistently evoked time-locked cortical potentials with early and late components at approximately 40 ms and 100 ms post-stimulation, and impedance monitoring confirmed electrode functionality and stability throughout. Strength-duration analysis revealed rheobase and chronaxie values, providing a quantitative basis for parameter selection and comparison with established intracranial stimulation modalities.

Baylor College of Medicine researchers analyzed more than 200 hours of intracranial recordings from eight patients with treatment-resistant obsessive-compulsive disorder, five of whom also had bilateral orbitofrontal ECoG electrodes. Neural signals alone did not decode symptom severity or momentary distress above chance; adding facial and speech features raised performance to R=0.73 for severity and R=0.34 for distress. The medRxiv preprint has not been peer reviewed.

Nova Southeastern University researchers combine intracranial ECoG network features with a support vector machine and Bayesian updating for real-time continuous seizure risk forecasting.