Brain Electrode Stimulation via Frequency Analysis
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Solution Overview
Problem
Current electrostimulation methods for identifying brain regions responsible for specific functions are complex, time-consuming, and can trigger epileptic seizures, especially in patients prone to them, and are challenging for children to describe their perceptions accurately.
Innovation Solution
An apparatus with a control unit that selectively applies electrical stimuli to the brain using a limited number of electrodes, featuring a stimulation unit, measuring unit, analysis unit, and selection/actuating unit to preselect electrodes based on signal analysis in specific frequency ranges, and includes a display unit for visualizing results and allowing manual or automated stimulation to avoid overstimulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all electrodes are activated to identify brain regions, then identification completeness is improved, but procedure complexity and time consumption increase
Solution Approach 1:
The analysis unit performs preliminary analysis of measurement signals from all electrodes to preselect only those electrodes showing abnormal signal patterns in the 60-1000 Hz range. This preliminary identification step allows the system to focus subsequent stimulation on a limited subset of relevant electrodes, reducing overall procedure complexity while maintaining identification completeness.
Solution Approach 2:
The procedure is divided into distinct phases: initial measurement signal acquisition from all electrodes, analysis and preselection of abnormal electrodes, and targeted stimulation of preselected electrodes. This segmentation allows the system to manage complexity by handling different electrode sets at different stages rather than activating all electrodes simultaneously throughout the entire procedure.
2Measurement precision
If stimulus amplitude is increased to induce reaction, then reaction detection sensitivity is improved, but risk of epileptic seizures increases
Solution Approach 1:
Instead of applying high-amplitude stimuli globally to all electrodes, the system applies stimulation locally only to electrodes that have been preselected based on abnormal signal patterns. This localized approach concentrates the necessary stimulus amplitude on specific brain regions showing pathological activity, improving reaction detection sensitivity while minimizing the overall risk of triggering epileptic seizures in other brain areas.
Solution Approach 2:
The measurement signals themselves guide the stimulation process by automatically identifying which electrodes require stimulation based on abnormal frequency content. The system uses the brain's own electrical activity patterns to determine where stimulation is needed, eliminating the need for blanket high-amplitude stimulation across all electrodes and thereby reducing seizure risk.
3Productivity
If multiple electrodes are stimulated simultaneously, then identification efficiency is improved, but difficulty in obtaining correct descriptions increases
Solution Approach 1:
Rather than stimulating all electrodes simultaneously, the system applies stimulation to a partial set of electrodes that are preselected based on abnormal signal characteristics. This partial action approach maintains identification efficiency by focusing on the most relevant brain regions while improving the ease of obtaining correct descriptions, as the test subject experiences fewer simultaneous stimuli and can more accurately report perceptions.
4Measurement precision
If frequency range 60-1000 Hz is analyzed, then abnormal signal detection precision is improved, but analysis complexity increases
Solution Approach 1:
The analysis unit focuses on a specific frequency parameter range (60-1000 Hz) that has been identified as containing abnormal brain signals. By concentrating analysis on this defined frequency window rather than analyzing the entire spectrum, the system achieves high detection precision for pathological signals while managing analysis complexity through parameter specification. The Fourier transformation efficiently processes signals within this bounded frequency range.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid and simple identification of brain regions responsible for specific functions with reduced stimuli, minimizing the risk of epileptic seizures and improving accuracy in perception descriptions from test subjects.
Implementation Method 1
The analysis unit (13) is designed for the purpose of analyzing the measurement signals (M) ascertained by the measuring electrodes (21) and preselecting individual ones of the electrodes (21) for the emission of a stimulus by means of this analysis on the basis of this analysis, in that the analysis unit (13) examines the measurement signals (M) at the measuring electrodes (21), in particular exclusively, for the presence of abnormal signals in the scope of a frequency analysis by means of a Fourier transformation
Data Source
AI summary
A method and an apparatus provide electro stimulation to a test subject. A number of electrodes are connected to the brain of a test subject, wherein the voltages present on the individual electrodes are measured and analyzed after the delivery of a stimulus. During a preselection based on the analysis, individual electrodes are selected for the delivery of a stimulus, wherein one electrode is selected from the individual preselected electrodes and the stimulus is delivered to the brain by the electrode. Accordingly, in the analysis using the measurement signals during the preselection, the signals present on the electrodes are examined, in particular exclusively examined, for the presence of signal power or signal energies in the range from 60 Hz to 1 kHz, in particular between 60 Hz and 180 Hz.