IEEE Transactions on Neural Systems and Rehabilitation Engineering

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.

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.

A hybrid brain-computer interface replaced crutch control for 10 participants walking with a custom lower-limb exoskeleton, classifying steady-state visual evoked potentials with 95.06% accuracy and reaching F1 scores of 99.80% and 99.22% for its wink- and clench-triggered asynchronous switches, researchers reported in IEEE TNSRE. Measured against crutch control, the system scored 78.25 versus 53.50 on the System Usability Scale and 3.15 versus 7.85 on NASA-TLX physical demand.

Researchers report that a BCI-controlled sixth-finger neurofeedback intervention improved motor function in stroke: after 8 sessions (2 weeks) of motor-imagery BCI training, 14 patients gained an average of 7.9 points on FMA-UE and 7.1 on the Barthel Index, with 9 of 14 reaching the 6.6-point minimally clinically important difference. EEG tracking across the full intervention showed a two-phase ERD trend that strengthened in week one and narrowed to the contralateral sensorimotor area in week two, and resting-state functional connectivity rose afterward, correlating with motor gains. The authors say the work offers longitudinal evidence on neuroplasticity in stroke rehabilitation.