A Nearly Packaging-Free Design Paradigm for Light, Powerful, and Energy-Dense Primary Microbatteries.

Billions of internet connected devices used for medicine, wearables, and robotics require microbattery power sources, but the conflicting scaling laws between electronics and energy storage have led to inadequate power sources that severely limit the performance of these physically small devices. Reported here is a new design paradigm for primary microbatteries that drastically improves energy and power density by eliminating the vast majority of the packaging and through the use of high-energy-density anode and cathode materials. These light (50-80 mg) and small (20-40 µL) microbatteries are enabled though the electroplating of 130 µm-thick 94% dense additive-free and crystallographically oriented LiCoO2 onto thin metal foils, which also act as the encapsulation layer. These devices have 430 Wh kg-1 and 1050 Wh L-1 energy densities, 4 times the energy density of previous similarly sized microbatteries, opening up the potential to power otherwise unpowerable microdevices.

Seizure localization using EEG analytical signals.

OBJECTIVE: Localization of epileptic seizures, usually characterized by abnormal hypersynchronous wave patterns from the cortex, remains elusive. We present a novel, robust method for automatic localization of seizures on the scalp from clinical electroencephalogram (EEG) data. METHODS: Seizure patient EEG data was decomposed via the Hilbert Transform and processed through the following methodology: sorting the analytic amplitude (AA) in the time instance, locating the maximum amplitude within the vector of channels, cross-correlating amplitude values in the time index with the channel vector. The channel with highest AA value in time was located. RESULTS: Our approach provides an automated way to isolate the epi-genesis of seizure events with 93.3% precision and 100% sensitivity. The method differentiates seizure-related neural activity from other common EEG noise artifacts (e.g., blinks, myogenic noise). CONCLUSIONS: We evaluated performance characteristics of our source location methodology utilizing both phase and energy of EEG signals from patients who exhibited seizure events. Feasibility of the new algorithm is demonstrated and confirmed. SIGNIFICANCE: The proposed method contributes to high-performance scalp localization for seizure events that is more straightforward and less computationally intensive than other methods (e.g., inverse source modeling). Ultimately, it may aid clinicians in providing improved patient diagnosis.

Quantitative EEG Signatures through Amplitude and Phase Modulation Patterns.

Cortical spatiotemporal signal patterns based on object recognition can be discerned from visual stimulation. These are in the form of amplitude modulation (AM) and phase modulation (PM) patterns, which contain perceptual information gathered from sensory input. A high-density Electroencephalograph (EEG) device consisting of 48 electrodes with a spacing of 5 mm was utilized to measure frontal lobe activity in order to capture event-related potentials from visual stimuli. Four randomized stimuli representing different levels of salient responsiveness were measured to determine if mild stimuli can be discerned from more extreme stimuli. AM/PM response patterns were detected between mild and more salient stimuli across participants. AM patterns presented distinct signatures for each stimulus. AM patterns had the highest number of incidents detected in the middle of the frontal lobe. Through this work, we can expand our encyclopedia of neural signatures to object recognition, and provide a broader understanding of quantitative neural responses to external stimuli. The results provide a quantitative approach utilizing spatiotemporal patterns to analyze where distinct AM patterns can be linked to object perception.