Isabel Gerrard

In 2025 July, the third-ever interstellar object, 3I/ATLAS, was discovered on its ingress into the Solar System. Similar to the NASA Voyager missions sent in 1977, science probes by extraterrestrial life (artifact "technosignatures'") could be sent to explore other stellar systems like our own. In this campaign, we used the SETI Institute's Allen Telescope Array to observe 3I/ATLAS from 1--9~GHz. We detected nearly 74 million narrowband hits in 7.25~hr of data using the newly-developed search pipeline bliss. We then applied blanking in frequency and drift rate to mitigate Radio Frequency Interference (RFI) in our dataset, narrowing the dataset down to 2 million hits. These hits were further filtered by the localization code NBeamAnalysis, and the remaining 211 hits were visually inspected in the time-frequency domain. We did not find any signals worthy of additional follow-up. Accounting for the Doppler drift correction and given the non-detection, we are able to set an Effective Isotropic Radiated Power (EIRP) upper limit of ~W on radio technosignatures from 3I/ATLAS across the frequency and drift rate ranges covered by our survey.

Next-generation human-computer interaction (HCI) is moving towards more seamless, intuitive,
and personal modes of communication, redefining how we interact with technology and one another.
Within this landscape, silent speech recognition (SSR) offers a powerful new interaction paradigm,
enabling hands-free, private interaction while supporting individuals with speech impairments and
enabling communication in noisy or sensitive environments. Recent advances in miniaturized sensors and artificial intelligence (AI) have accelerated the development of more sophisticated wearable
SSR systems, driven by growing demand for effortless and accessible communication. Although
electrophysiological (ExG) modalities, particularly electromyography (EMG), have dominated early
efforts in developing wearable SSR, critical challenges remain. Limited generalizability across
users, sensor-skin interface issues, and difficulties with the comfort of use are all current roadblocks
to reliable, high-fidelity signals in a wearable form factor. We propose that magnetometers offer
a promising alternative to ExG and have the potential to unlock more robust, generalizable, and
user-friendly SSR systems. We demonstrate that magnetometers embedded in a headphone form
factor achieve a per-user SSR accuracy of 86%, significantly outperforming previously reported
state-of-the-art wearable headphones combining ExG and inertial measurement units (IMUs). In
addition, we show that wearable magnetometry enables generalization across individuals for SSR.
Extending beyond headphones, we also introduce a necklace form factor with magnetometers that
is capable of decoding both silent and overt speech in ambient conditions, further showcasing the
versatility of magnetometers across different wearable designs in real-world conditions.

Introduction: The normal transition to sleep is characterized by a reduction in higher frequency activity and an increase in lower frequency activity in frontal brain regions. In sleep onset insomnia these changes in activity are weaker and may prolong the transition to sleep.

Methods: Using a wearable device, we compared 30min of short duration repetitive transcranial electric stimulation (SDR-tES) at 0.75Hz, prior to going to bed, with an active control at 25Hz in the same individuals.

Results: Treatment with 0.75Hz significantly reduced sleep onset latency (SOL) by 53% when compared with pre-treatment baselines and was also significantly more effective than stimulation with 25Hz which reduced SOL by 30%. Reductions in SOL with 25Hz stimulation displayed order effects suggesting the possibility of placebo. No order effects were observed with 0.75Hz stimulation. The decrease in SOL with 0.75Hz treatment was proportional to an individual’s baseline wherein those suffering from the longest pre-treated SOLs realized the greatest benefits. Changes in SOL were correlated with left/right frontal EEG signal coherence around the stimulation frequency, providing a possible mechanism and target for more focused treatment. Stimulation at both frequencies also decreased perceptions of insomnia symptoms measured with the Insomnia Severity Index, and comorbid anxiety measured with the State Trait Anxiety Index.

Discussion: Our study identifies a new potential treatment for sleep onset insomnia that is comparably effective to current state-of-practice options including pharmacotherapy and cognitive behavioral therapy and is safe, effective, and can be delivered in the home.

The progression of human-computer interfaces into immersive and touchless realities requires new ways of interacting with machines that are correspondingly intuitive and seamless. Among these are gesture-based systems that use natural hand movements to interact with and control digital devices. Today, these systems are most commonly implemented through the use of cameras or inertial sensors, which have drawbacks in environments that are poorly lit, in conditions where the hands are obscured, or for applications that require fine motor control. More recent studies have advocated for the use of surface electromyography (sEMG) to capture gesture information by sensing electrical activity generated by muscle contraction. While promising demonstrations have been shown, studies have also outlined limitations in sEMG when it comes to generalization across a population, largely due to physiological differences between individuals. Magnetomyography (MMG) is an alternative modality for measuring the same motor signals at the muscle, but is impervious to distortions caused by tissue, hair, and moisture; this indicates potential for lower variability caused by physiological differences and changes in skin conductivity, making MMG a promising generalizable solution for gesture control. To test this theory, we developed wristbands with magnetic sensors and implemented a signal processing pipeline for gesture classification. Using this system, we measured MMG across 30 participants performing a gesture task consisting of nine discrete gestures. We demonstrate average single-participant classification accuracy of 95.4%, rivaling state-of-the-art accuracy with sEMG. In addition, we achieved higher cross-session and cross-participant accuracy compared to sEMG studies. Given that these results were obtained with a non-ideal recording system, we anticipate significantly better results with better sensors. Together, these findings suggest that MMG can provide higher performance for control systems based on gesture recognition by overcoming limitations of existing techniques.

The measurement of magnetic fields generated by skeletal muscle activity, called magnetomyography (MMG), has seen renewed interest from the academic community in recent years. Although studies have demonstrated complex models of MMG and experiments classifying between different movements using MMG, there has yet to be time frequency analysis of MMG as well as concurrent recordings of MMG and its electrical counterpart, surface electromyography (sEMG). Here, we aim to better understand MMG in the context of sEMG by simultaneously recording both modalities during various muscle contraction tasks. We found that, similar to sEMG, MMG shows highly linearly correlated power to the degree of muscle contraction, has a unimodal distribution in spectral power, and can detect changes in muscle fatigue via changes in the spectral distribution. One main difference we found was that MMG typically has more high frequency content compared to sEMG, even when accounting for the filtering induced by the size of the sEMG electrodes. We additionally demonstrate empirically the decrease in MMG power due to distance from the arm and show MMG decreases slower than the inverse square law and can be measured up to 50 mm from the surface of the skin. Finally, we were able to capture MMG with non-OPM sensors showing that sensor technology has made great strides towards enabling MMG applications.