Wearable photoplethysmography sensors extract blood volume signal features to predict seizures up to ten minutes in advance, replacing invasive EEG monitoring.
A communication device transmits user situation data to nearby devices via short-range links.
Parallel decoders execute user intention and detect neural error signals in real-time to automate brain-machine interface control.
Segmenting portable ultra-low-field magnets from stationary control units overcomes high costs and limited accessibility of traditional high-field MRI systems.
Segmented data quantification resolves complexity trade-offs by assessing efficacy and adverse effects through automated feedback loops.
A hearing aid system monitors auditory evoked potentials using integrated electrodes to automate fitting.
Upward-extending members engage hair to position sensors, resolving the contradiction between mobile usability and obtrusive device structure.
A multi-channel signal processing method computes a data condition number to identify anomalies within the acquired signal stream.
A transcranial stimulation system computes electrical field montages by aligning current with white matter tract orientation to transform brain states.
A depth cooling implant uses a heat pipe to transmit cooling signals along its structure.
A seizure prediction device detects high-frequency oscillations in the CA3 hippocampal region to enable early intervention.
A movable optical sensor adjusts position to prevent excessive pressure that degrades blood flow detection accuracy.