Brain Computer Interface Signal Detection Using Pseudo-Random Noise
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Solution Overview
Problem
Existing brain-computer interface (BCI) systems face challenges in reliably detecting mental processing of stimuli due to interference from noise and complexity in signal analysis, particularly in distinguishing stimulus-related activity from spontaneous brain oscillations.
Innovation Solution
The method employs pseudo-random noise components as stimuli tags, which are correlated with brain wave signals to enhance detection robustness, using spread spectrum techniques to distribute power across a wide frequency range, and orthogonal Golden codes to minimize interference, allowing for reliable tracking of mental processing and selective attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional stimulus detection methods are used in BCI systems, then the system structure remains simple, but the reliability of stimulus detection deteriorates due to noise interference and difficulty in distinguishing stimulus-related activity from spontaneous brain oscillations
Solution Approach 1:
The patent introduces pseudo-random noise components as intermediary tags that are embedded in stimuli and correlated with brain wave signals. These tags act as mediators between the stimulus and the detection system, enabling reliable identification of stimulus-related neural responses even in the presence of spontaneous brain oscillations and noise. The correlation process selectively amplifies tagged stimulus responses while suppressing uncorrelated background activity.
Solution Approach 2:
The patent transforms the detection approach by changing the parameter space from direct signal amplitude analysis to correlation-based detection in the frequency domain. By spreading the stimulus tag power across a wide frequency range using pseudo-random noise sequences, the system can detect stimuli through correlation peaks that stand out against the background noise spectrum, significantly improving detection reliability.
2Reliability
If spread spectrum techniques with pseudo-random noise components are used to tag stimuli, then the reliability and robustness of stimulus detection improves, but the complexity of signal processing and analysis increases
Solution Approach 1:
The patent applies preliminary action by pre-generating and storing the pseudo-random noise tag sequences that will be used to modulate stimuli. These tag sequences are prepared in advance and made available for correlation processing, allowing the detection system to efficiently identify stimulus responses without requiring complex real-time analysis. The pre-computed tags enable rapid correlation-based detection.
Solution Approach 2:
The patent replaces traditional time-domain signal analysis methods with frequency-domain correlation processing. Instead of manually analyzing complex time-varying brain wave signals for stimulus responses, the system uses automated correlation computation in the frequency domain, which efficiently separates stimulus-related activity from background oscillations through spectral matching with the pseudo-random noise tags.
3Measurement precision
If correlation analysis with pseudo-random noise tags is applied to brain wave signals, then the ability to distinguish stimulus-related activity from background noise improves, but the computational requirements and data processing load increase
Solution Approach 1:
The patent employs periodic action through the use of pseudo-random noise sequences with known statistical properties and repeating patterns. These periodic tag sequences allow the correlation process to accumulate signal energy over multiple stimulus presentations, improving measurement precision through averaging while maintaining computational efficiency. The periodic structure enables systematic processing of brain wave data across multiple trials.
Solution Approach 2:
The patent applies partial action by focusing correlation analysis only on specific frequency bands and time windows where stimulus-related activity is expected, rather than processing the entire brain wave spectrum. This selective approach reduces computational load while maintaining high measurement precision for the relevant neural responses, avoiding unnecessary computation on unrelated frequency ranges.
Data Source
AI summary
Method and brain computer interface for processing a brain wave signal of a subject using a brain wave detector. One or more stimuli are applied to the subject which each include a pseudo random noise component. The brain wave signal is detected and mental processing of the one or more stimuli is tracked by correlating the pseudo random noise component and the brain wave signal.