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A research team in Poland used just two occipital electrodes (O1 and O2) on an EEG headband to distinguish four classes of eye events (blinks, left saccades, right saccades and a neutral state) with 72.37% test accuracy. Instead of training a single stronger model, they split four one-dimensional convolutional neural networks into binary experts and used stacked generalization, letting a meta-classifier resolve conflicts between them. Notably, the experts that performed worst on their own produced the best results when combined. The work was published by Neurotechnology, an EEG hardware maker whose BrainAccess brand makes consumer EEG headbands; HALO is its four-channel portable headband, and the accompanying software is free. Eye tracking usually requires dedicated equipment, and this study shows that two occipital channels on a consumer EEG headband can also pick up blinks and saccades. But the data come from only 4 healthy participants and 1,284 samples, so the approach is still a long way from replacing an eye tracker.

Translating scalp EEG directly into free-form text has long been seen as one of the most ambitious goals for non-invasive brain-computer interfaces. But when researchers fed random noise instead of real EEG into several published decoders, some models still produced fluent sentences and scored about as well as they did on real brain data, suggesting the language model was doing most of the work. The field has since made noise-baseline tests and decoding without teacher forcing standard validation practice, and has used magnetoencephalography (MEG) as a comparison to quantify how far EEG trails cleaner signals.