
Sleep wearable firms trained AI on customer brainwaves without clear consent
Investigators found tens of millions of hours of EEG; Interaxon trained a model on about 50,000 hours.
10 stories, newest first · one card per story

Investigators found tens of millions of hours of EEG; Interaxon trained a model on about 50,000 hours.
A 30M EEG encoder leads 8 of 9 metrics versus BENDR, BIOT, LaBraM and EEGPT, and RALF lifts seizure sensitivity from 61.4% to 74.4%.
A novel hierarchical alignment framework retrieves read sentences from EEG signals and context for brain-computer interfaces.
S25-TRADES reached 23.06% mean true-union robustness versus 21.27% for uncompressed Full EEGNet.
The public EEGaze 140 set pairs EEG and eye tracking from 36 people across 140 realistic scenes.
Researchers introduce DyssomniaNN, a spiking model of five sleep-disorder cohorts showing sleep time poorly predicts restedness.
An EEG masking ablation matches REVE-level decoding performance at a fraction of the pre-training compute.
A research paper on low-precision EEG decoders examines adversarial robustness across compression techniques.
Case study applies Gaussian and Laplace differential privacy to clinical EEG features, quantifying privacy-utility trade-offs across three deployment scenarios.
Researchers characterize NERVE, five attack dimensions against AI-powered brain-computer interfaces, and release the EEGle framework uncovering 17 attack instances.
Today 1.5K readers · 2.4K views · 754 reads · all time 189K views