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Wearable neurotechnology and BCI applications, from assistive interfaces to clinical monitoring, favor single-channel EEG because it needs few electrodes and is light to wear. But muscle, eye and motion artifacts are hard to remove from a single channel, especially without auxiliary channels, artifact labels or hand-picked clean baseline segments. SPAR-EEG needs none of these: it applies three artifact-specific attenuation passes to each EEG segment, one based on variational mode decomposition for high-frequency muscle bursts and two based on singular spectrum analysis for blink-like ocular transients and slow motion drift. It achieved the best artifact-region SNR improvement at all 26 SNR levels tested and raised final accuracy by 7.8 percentage points in a P300 speller task using only the FP1 and FP2 leads.

The US Food and Drug Administration has cleared EEG Analysis Software from Ceribell, a US medical device company, through the 510(k) pathway after finding it substantially equivalent to a device already on the market. The software performs quantitative analysis of EEG signals and is regulated as a Class II device under the Neurology review panel. A 510(k) clearance only requires showing equivalence to an existing product, a lower bar than the premarket approval required for high-risk devices. The FDA record does not describe the software's indications or specific analysis functions.

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.

Researchers developed a non-invasive EEG brain-computer interface using auditory inputs and visual feedback to target phonological processing in developmental dyslexia, initially testing it in adults with and without dyslexia. Participants completed 16 sessions and underwent phonological awareness and reading assessments before and after training. Among participants who learned the BCI, including both dyslexic and control participants, offline BCI scores were significantly associated with improvements in nonword reading. BCI learning also showed significant associations with gains in real-word reading and amplitude rise time discrimination. These associations do not establish causation or demonstrate benefits in infants or children. This study is a preprint and has not been peer reviewed.

In three participants with tetraplegia imagining object manipulation, neural activity across several brain regions represented the configuration of the whole hand during imagined grasps. The posterior parietal cortex simultaneously encoded grasp and object properties, largely through distinct neuronal subpopulations. Grasp-related activity varied with motor planning versus imagined execution and with object presence versus absence; some regions also represented object shape during planning. The authors suggest that separating grasp and object information could support more stable BCI grasp control. However, the motor imagery task does not establish restored hand function or improved robotic grasp performance. The study is a preprint that has not been peer reviewed.

Researchers introduced a framework for analyzing neural traveling waves and identified oppositely directed propagation modes in marmoset electrocorticography and human EEG recordings. The waves were concentrated in specific spatial and temporal frequency ranges, involving alpha and low-beta activity, with propagation speeds the authors considered physiologically plausible. The framework analyzes temporal frequency, spatial frequency, and propagation direction, accommodates irregular sensor layouts, and helps distinguish traveling from standing waves. The study also introduces a single-trial detection method. These findings concern animal and human neural signal analysis, not demonstrated improvements in BCI control or clinical outcomes. The work is a preprint that has not been peer reviewed.

The MINTS protocol plans to enroll 21 people who are at least 6 months post-stroke and have persistent upper-limb motor impairments. They will complete 14 home-based motor imagery neurofeedback sessions, imagining movements while receiving feedback on brain activity. Training will use mobile EEG every second day. The single-group study will repeat measurements during baseline periods of different lengths and throughout training to track brain activity and motor performance. Primary assessments concern EEG activity and a measure of functional connectivity, alongside brain imaging, motor testing, daily-life movement monitoring, and an exploratory three-month follow-up. This protocol has not been peer reviewed and reports no training outcomes.

Researchers developed FUSE, a two-phase learning framework that combines simultaneous EEG and MEG recordings for motor imagery classification, and evaluated it on data from 20 participants. Learned representations increased median classification accuracy from 0.734 with conventional handcrafted spectral features to 0.794. The combined approach also exceeded MEG alone at 0.680 and EEG alone at 0.765, although the gain over EEG was modest. In transfer tests, more than half of cross-subject models performed within 0.01 accuracy of their subject-specific counterparts. These findings concern signal classification and representation transfer, not clinical efficacy. This study is a preprint and has not been peer reviewed.

Researchers introduced NeuroGate, a cross-regional signal translation framework that predicts an unrecorded brain region's waveform from a recorded region. Across 56 pathways spanning rodent local field potentials, human stereo-EEG and electrocorticography, and scalp EEG, its waveform predictions were more accurate than those of 13 deep-learning and linear baselines. To test directionality, the researchers silenced piriform-cortex output. Piriform-to-bulb predictions collapsed in all 8 folds, while reverse predictions were preserved. The authors interpret this as consistent with monosynaptic anatomy and demonstrate applications in assessing recording redundancy and testing directional translation. This study is a preprint and has not been peer reviewed.