Journal of Neural Engineering

A participant with severe upper-limb paralysis caused by amyotrophic lateral sclerosis (ALS) wore an endovascular stent-electrode array and a scalp EEG cap at the same time, in the same session, while attempting ankle flexion and extension. Both recordings changed markedly during attempted movement, but the stent array showed stronger per-channel motor modulation and was unaffected by skull attenuation. Scalp EEG was more susceptible to eye blinks and jaw-muscle activity, while the stent array picked up prominent cardiac signals. Neither method reliably distinguished left from right ankle movement, leaving spatial localization an open problem.

During removal of a right parafalcine meningioma, surgeons placed four Layer 7 μECoG arrays, 4,096 electrodes in total, on either side of the central sulcus and recorded somatosensory evoked potentials under 5 contralateral stimulation conditions. Using a 2 MΩ impedance cutoff, 3,739 channels were usable, a 91.3% yield; phase-reversal latencies were 19, 21, 25, 26 and 25 ms, consistent with the standard intraoperative mapping performed in the same operation. The researchers caution that the setup provides dense spatial sampling rather than submillimeter physiological resolution: measured responses were correlated across roughly 3–4 mm of cortex.

Participants steering a virtual avatar with a brain-computer interface reported a stronger sense that the avatar's body was their own when they imagined jogging than when they imagined opening their right hand, even though jogging imagery produced weaker EEG signals. Forward movement was driven by motor imagery-related sensorimotor rhythm event-related desynchronization from scalp EEG and direction by eye gaze, as participants guided a jogging avatar along a curved course before rating their embodiment in questionnaires. The researchers say neural signal strength and embodied experience can diverge, so the congruence between imagery and action should be weighed alongside standard decoding metrics.

Even when the eyes stay fixed on one point, the brain tracks moving objects in a video, and that tracking grows stronger with attention. But researchers at KU Leuven found that EEG tracking of an object's motion weakens the farther the object sits from the fixation point, meaning decoding methods that read attention from tracking strength may mistake where an object is for where attention is.

Speech imagery (SI) brain-computer interface results may be hard to reproduce, a University of Essex team reports. When the researchers re-ran published decoding pipelines, classification accuracy averaged 11.25 percentage points below the original reports, with gaps ranging from 2% to 39%; in a replication analysis, only 36% of SI participants cleared the threshold for statistical significance, against 91% in motor imagery (MI) datasets. Published in the Journal of Neural Engineering on September 4, 2026, the study is the first to assess both the reproducibility and the replicability of SI decoding. Every SI study evaluated was missing methodological details, and the authors conclude that SI's feasibility as a practical BCI paradigm may have been overestimated, a warning for clinical applications and follow-up research that rely on it.

Researchers at Northwestern Polytechnical University propose SEDAT, a hybrid tokenizer for large EEG foundation models that folds squeeze-and-excitation spatial aggregation, data-adaptive Gaussian average filtering, instantaneous-frequency-guided segmentation and Fourier-domain resampling into a single efficient pipeline. Evaluated on 10 heterogeneous EEG datasets with four foundation models, LaBraM, EEGFormer, EEGPT and NeuroGPT, SEDAT improved classification by up to 15.3% over fixed-length windowing and by 1.2-4.6% over the next-best tokenizer, the authors report. The study was published in the Journal of Neural Engineering on September 1, 2026.

Researchers at the University of Colorado Boulder have built ERP-XTTN, a cross-attention model that classifies event-related potentials (ERPs) in users it has never seen, with no per-user calibration. Across three public datasets and eight ERP components, it trailed the best baseline by 0.025 AUROC on average using only three channels. The work appeared in the Journal of Neural Engineering.

EEG signals are only microvolts strong, so blinks, jaw clenching and muscle activity easily contaminate them. Researchers at Tianjin University and Tiangong University in northern China proposed MTGNet, a framework that suppresses these artifacts while preserving the information downstream tasks need. On the public EEGDenoiseNet dataset, it cut spectral relative root-mean-square error by 18.9%, 31.5% and 14.0% for EMG, EOG and mixed artifacts respectively; on a real-world fatigue EEG dataset, it raised classification accuracy by 6.20 to 6.69 percentage points over unprocessed input. Adapting the framework to a new task takes only 0.33 million low-rank adaptation (LoRA) parameters and no paired clean EEG reference.

A workshop at the BCI Society Meeting 2025, run with the Implantable BCI Collaborative Community (iBCI-CC), took up how clinical outcome assessments (COAs) for pivotal BCI trials should be selected, developed and validated. Participants pointed to patient heterogeneity, the absence of widely validated COAs, and the difficulty of capturing outcomes that matter in home and daily-life settings. The discussion lays groundwork for the iBCI-CC Clinical Study Endpoints Workgroup to build a transparent process for identifying meaningful aspects of health and concepts of interest, in support of regulatory approval and reimbursement.

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.

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.

Published in Journal of Neural Engineering on August 7, 2026, the study analyzed human electrocorticography data from four subjects to investigate medial wall contributions to finger movement decoding. Significantly above-chance finger movement detection was observed across multiple medial wall subregions, with local motor potentials and oscillatory power in the 8-12 Hz and 12-34 Hz bands contributing most strongly. Feature dynamics shared key properties with primary motor cortex, including pre-movement desynchronization, while also exhibiting region-specific positive or negative LMP modulations. Medial wall channels in two subjects enabled significant differentiation between individual fingers, and one subject showed decoding of both contralateral and ipsilateral finger movements, though this is a single case and preliminary.

The study proposes BiGSTF-Net, a multimodal architecture that exploits the complementary properties of EEG and functional near-infrared spectroscopy (fNIRS) for cognitive-state decoding: heterogeneous spatio-temporal extractors capture each modality's representations, a modal residual interaction unit provides bidirectional cross-modal guidance, and a spatio-temporal gating unit fuses intra-modal features. Under cross-session evaluation on multiple BCI datasets, BiGSTF-Net consistently outperformed representative multimodal fusion baselines; ablations validated each component and visualizations matched the known neurophysiological features of the two signals.

The study recorded hippocampal CA1 population activity in rats performing a sequential spatial decision task in a modified T-maze, dividing the decision into initiation, running and approach phases. Local theta sequences consistently over-represented the actual choice, while prospective representations driven by choice-arm place cells shifted from predicting the actual choice during running to representing potential paths more evenly at the choice point. Integrating local and prospective features improved decoding, reaching 74.4% accuracy for future choice prediction and 78.2% for upcoming trajectory decoding.

The study presents a spatial harmonization framework built on a two-layer graph convolutional network (GCN) that maps heterogeneous EEG recordings onto a unified physical electrode layout while preserving motor imagery information, addressing electrode-configuration mismatches across MI-BCI datasets. Each trial is modeled as a graph so the GCN captures spatio-temporal relations, and harmonized EEG showed lower error than spherical spline interpolation while retaining the key temporal-spectral-spatial features. Combining real and harmonized EEG lifted EEGNet accuracy from 56.57% to 66.20% and FBCNet from 61.96% to 72.54% in within-session classification on Dataset A, and supported source-only cross-dataset transfer and target-domain fine-tuning.