7 articles
A comprehensive review maps how brain signals measured during stroke recovery are converging with BCI systems and closed-loop neuromodulation to move rehabilitation beyond generic protocols toward individualized treatment.
New framework resolves the historic trade-off between acoustic richness and linguistic clarity in neural speech decoding, achieving 4.0/5.0 intelligibility with minimal training data.
Researchers developed TFSANet, a deep learning model that decodes language comprehension from EEG signals, achieving 60.73% accuracy in aphasia patients using Chinese linguistic stimuli.
New fMRI research identifies precise cortical locations for implantable BCI electrodes, finding that surface recordings from gyral regions may suffice for gesture decoding without penetrating sulcal depths.
Researchers adapted automated fuzzing from software security to stress-test machine learning models that control neural implants, revealing widespread violations of biophysical safety limits in retinal and cortical devices.
New research reveals how targeting different brain regions with theta burst stimulation enhances motor imagery performance through separate mechanisms, offering potential solutions for stroke patients struggling with BCI control.
Researchers demonstrate that individualized fMRI models can decode spontaneous pain in chronic pain patients, marking a shift toward objective, brain-based pain measurement that could reshape treatment and neurotech interventions.
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