BCIwiki — Brain-computer interface news, research and industry database

Researchers introduced a method for selecting signal lags to identify when one signal's history is most useful for predicting another. Across three benchmark tasks using existing monkey-experiment data, the selected lags achieved the highest or equal-best coefficient of determination, R², with mean R² improving by 4.1% relative to the strongest competing baseline. Called ATEMPO, the method estimates information relationships across history depths and temporal positions with a single fit, reducing the need for repeated fitting. On datasets with known lags, its lag-detection accuracy was 0.98±0.04. The findings concern dataset prediction performance, not clinical efficacy.

Researchers from Hengyang Normal University and Chongqing University of Education in China jointly proposed NCEL, a lightweight EEG decoder evaluated on five benchmark datasets. It achieved 82.75% mean classification accuracy on BCIC IV-2b. Relative to EEGNet reference values, mean performance improved by 12.53 percentage points on P300 and 9.54 percentage points on BCIC IV-2b, with approximately 0.008M parameters. However, additional subject-specific evaluations with disjoint sessions found cross-session performance comparable to EEGNet on BCIC IV-2a and a mean improvement of 1.80 percentage points on BCIC IV-2b. The study demonstrates classification capabilities in a compact model, while broader cross-session generalization still needs validation.

Hanix United says the Shenzhen Brain-Computer Interface Industry Association was officially established at the opening of the 2026 Shenzhen brain science expo, with the company serving as its chair unit. A Greater Bay Area collaborative innovation consortium for BCI development was also launched. The conference opened on October 21, 2026, at Shenzhen Guangming Life Science Park. According to the company, its exhibits included a deep transcranial magnetic stimulation system, a near-infrared brain imaging device, a 3D navigation system for magnetic stimulation, its Smart series magnetic stimulators, and EEG/event-related potential products.

A CUHK-Shenzhen collaboration introduced OneEEG for multitask EEG question answering. Standard 19-channel tasks use numerical signals and waveform images; non-standard tasks use images only. On the closed-set OneEEG-Bench, Overall accuracy reached 65.67% and Macro accuracy 68.24%, versus 50.39% Overall for baseline GPT-5.5. OneEEG received EEG-specific training; baselines used only images and text. These differences preclude interpreting the result as a general model or clinical diagnostic ranking.

Researchers from Beijing University of Technology and collaborating institutions proposed REFS to select EEG features for multi-dimensional emotion recognition when some emotional labels are missing. The method reconstructs incomplete labels while reducing redundancy among selected features, addressing modeling challenges posed by high-dimensional features and relatively few high-quality samples. Tests used DREAMER, DEAP and HDED, three datasets with different EEG channel configurations, at varying label-missing ratios. REFS consistently outperformed the 13 advanced feature-selection methods compared in robustness under these conditions. These dataset findings do not establish performance in deployed affective BCIs.

A collaboration involving UC Berkeley, USC and Microsoft Research introduced MtReNIRS, pretrained on 12 public fNIRS datasets covering 268 subjects and about 567 recording hours, then evaluated on three unseen datasets. It aligns probe layouts for cross-subject transfer and within-subject calibration with two to eight labels per class. Cross-subject results were not uniformly significantly better than fully supervised baselines, and few-shot gains varied by dataset. Device effects were not isolated because device, task and montage differences co-occurred.

Researchers introduced ORBITAL, a framework for compressing EEG decoders while evaluating resistance to adversarial attacks and actual hardware costs. Across nine subjects and three random seeds on BCI Competition IV-2a, S25-TRADES achieved the highest observed mean true-union robustness among the tested compressed EEGNet models: 23.06%, versus 21.27% for standard Full, with 26.27% fewer parameters and 25.10% fewer multiply-accumulate operations. Hardware tests also showed that lower numerical precision did not necessarily save energy or time: native INT8 incurred about twice the FP16 latency and energy. These results do not establish comprehensive reliability in real-world use.

A patient with chronic complete spinal cord injury improved from AIS A to AIS C and regained partial voluntary movement after one year of combined neuromodulation and conventional rehabilitation. The 53-year-old man had a T9 injury and received sequential spinal cord electrical stimulation, BCI training, motor cortex stimulation, and peripheral magnetic stimulation of both iliopsoas muscles, alongside 12 months of rehabilitation. His left lower limb motor score rose from 0 to 6, spontaneous defecation and penile erection returned, and his neuropathic pain score fell from 8 to 0. This uncontrolled case suggests a possible association with functional recovery but cannot distinguish the contributions of individual interventions or exclude spontaneous neurological recovery.

A review from Qingdao University examines how flexible brain-computer interface electrodes are fabricated and optimized for signal recording and long-term stability. Mechanical mismatch between rigid electrodes and brain tissue may cause tissue damage and reduce recording stability during long-term implantation; flexible electrodes offer better mechanical compatibility. The review covers microelectromechanical systems, printing and transfer, laser processing, thermal drawing, and electrospinning, alongside deposition and surface modification for performance enhancement. It also identifies challenges in large-scale manufacturing, long-term stability, and the physical and clinical limitations of electrodes with different levels of invasiveness. This is a technology review, not a clinical efficacy study of a new device.