How Apple's AI could enable AirPods to read brain signals
What's the story
Apple researchers have developed a novel method for training an artificial intelligence (AI) model to understand one aspect of brain electrical activity without any annotated data. The technique, called PAirwise Relative Shift (PARS), is detailed in a study titled "Learning the relative composition of EEG signals using pairwise relative shift pretraining." This innovative approach, while not directly linked to AirPods in the study, could potentially inform future developments in wearable technology, such as AirPods, to interpret brain signals.
Methodology
PARS: A self-supervised learning approach
PARS is a self-supervised learning (SSL) technique that learns electroencephalography (EEG) representations from unlabeled data. It minimizes the need for costly annotations in clinical applications like sleep staging and seizure detection. The study highlights that while current EEG SSL methods mainly use masked reconstruction strategies, position prediction pretraining remains underutilized despite its ability to learn long-range dependencies in neural signals.
Innovation
PARS pretraining: A novel approach to EEG signal analysis
The researchers proposed PARS pretraining, a unique pretext task that predicts relative temporal shifts between randomly sampled pairs of EEG windows. Unlike reconstruction-based methods focusing on local pattern recovery, PARS encourages encoders to capture relative temporal composition and long-range dependencies inherent in neural signals. The study's results show that transformers pretrained with this method consistently outperform existing strategies in label-efficient and transfer learning settings.
Applications
Potential applications in EEG analysis
The PARS-pretrained model was tested on four different EEG benchmarks, including Wearable Sleep Staging (EESM17), Abnormal EEG Detection (TUAB), Seizure Detection (TUSZ), and Motor Imagery (PhysioNet-MI). The results were promising as the model outperformed or matched previous methods on three of the four datasets tested. This suggests that the self-supervised learning method could be used to improve performance across a range of EEG analysis tasks.
Patent
Apple's patent application hints at future AirPods capabilities
In 2023, Apple filed a patent application for "a wearable electronic device for measuring biosignals of a user." The patent explicitly mentions ear-EEG devices as an alternative to scalp systems and proposes a solution to their limitations. It suggests packing more sensors than needed around the AirPods's ear tips and using an AI model to select electrodes with optimal signal quality.