Electroencephalography TRIZ Cases

A digital multiplexer circuit selects specific EEG channels and resolution parameters to optimize bandwidth usage while maintaining subject mobility.

A bioelectric neuro device uses a concentric electrode system to measure brain electrical activity for non-invasive neurological disorder treatment.

Segmenting EEG signals via bandpass filters resolves reliability gaps in anesthesia depth assessment by displaying distinct frequency bands.

A quantitative sleep assessment method using cross-spectral coherence of R-R intervals and ECG-derived respiration signals.

Interleaving probe and target trials maintains subject vigilance while measuring P300 brain waves, reducing countermeasure effectiveness.

Jagged dry electrodes eliminate gel drying and signal contamination by using titanium oxide coatings for stable capacitive coupling.

Replacing conventional BIS metrics with k-th order spectral analysis resolves false indications caused by NMDA antagonist-induced high frequency activity.

Automated classification of subcortical structures replaces subjective acoustic interpretation with objective color-coded maps derived from neural activity.

A bioelectrode uses an insulating fitting member and covering layer to prevent sweat-induced short circuits between wiring lines and the body.

Cortical evoked potentials serve as biomarkers to detect therapy effectiveness in electrical stimulation systems.

A computational model simulates human neural responses to evaluate therapeutic options.

Three triangular load cells capture directional force vectors to resolve signal dispersion and motion artifacts while measuring distraction levels.

A sensor supplies variable electric power to its communication interface based on detected physiological events.

Implantable medical device monitors physiological parameters to determine sleep quality metrics.

An electrode integrates abrasion elements with a flexible body to enable controlled skin contact.

Biomonitoring sensors detect operator stress to declutter cockpit displays, resolving information overload during high-stress flight operations.

A sleep depth index derived from frontal EEG signals and bioimpedance measurements detects REM periods to quantify sleep stages.

Gel electrode friction prevents accessory displacement during head movement, ensuring accurate brain wave measurement.

A helmet secondary visor reflects rear-facing camera images to improve situational awareness without requiring head movement.

A cross-phaseogram analysis system quantifies brainstem timing and phase information from complex auditory stimuli.

Fluorosulfonic acid salt adhesive maintains conductivity without water loss or skin irritation.

Dynamic sampling grids apply coarse or fine density based on local complexity, reducing spatial aliasing while maintaining measurement speed.

End imaging sensor in nasal tube images airway closures to resolve diagnostic accuracy versus patient comfort trade-offs.

Phase lag entropy analysis quantifies functional connectivity between cerebral regions, resolving calculation delays in anesthesia depth measurement.

Adaptive digital signal processing filters electromagnetic interference from MR scanners to maintain high-fidelity ECG monitoring.

An integrated testing system merges display and input functions to assess cognitive processing under realistic conditions.

Frequency band normalization and probability integration resolve inter-patient variability in EEG signals.

Axial splitting of the guide sheath removes the insertion tool after positioning, reducing device complexity while maintaining ease of operation.

A neurofeedback system ranks EEG leads by coefficient of variance to sequence low energy RF treatment delivery.

A neurostimulator applies personalized neuromodulation signals determined by machine learning models to prevent predicted seizures.

An integrated probe combines optical fibers and electrodes to correlate brain wave signals with optical measurements, reducing setup time.

Segmented electrode modules attach securely to headsets while flexible connectors isolate signal processing complexity from the sensor interface.

Automated evolutionary algorithms select features to build classifiers, eliminating manual selection errors and reducing time while maintaining accuracy.

A state-space multitaper framework processes electrophysiological signals to generate high-resolution spectral information.