EEG Curve Fitting for rTMS Stimulation Parameter

The patent transforms the static, fixed-frequency stimulation approach into a dynamic system where the pulse frequency is continuously adjusted to match the instantaneous dominant frequency of the EEG signal. This is achieved by computing the short-time Fourier transform (STFT) or other time-frequency analysis methods to track frequency drift, and then adapting the stimulation frequency in real-time to follow these transient variations, thereby maintaining optimal resonance conditions throughout the treatment session.

The patent changes the stimulation parameter (frequency) from a fixed value to a time-varying parameter that adapts to the EEG frequency. By monitoring the dominant frequency of EEG oscillations and adjusting the pulse frequency accordingly, the system maintains alignment with the brain's intrinsic oscillatory patterns, improving treatment efficacy while adding complexity to the protocol.

The patent implements a feedback loop where the EEG signal is continuously monitored, the dominant frequency is extracted through spectral analysis, and this information feeds back to adjust the stimulation frequency. This closed-loop control system automatically tracks and adapts to frequency drift, providing improved efficacy while managing complexity through automation rather than manual adjustment.

The patent performs preliminary frequency analysis of the EEG signal before and during stimulation to establish the baseline dominant frequency and track its evolution. By pre-computing the frequency characteristics and preparing the adaptation algorithm in advance, the system reduces the computational burden during real-time stimulation and simplifies the overall implementation complexity.

The patent enables the stimulation system to automatically monitor its own performance by tracking the EEG frequency and self-adjusting the pulse frequency without external intervention. The system serves itself by autonomously detecting frequency drift and correcting the stimulation parameters, thereby maintaining optimal efficacy while reducing the operational burden on the operator.

The patent implements automated feedback control where the system continuously monitors the EEG signal, detects dominant frequency changes, and automatically adjusts the stimulation frequency in response. This self-regulating mechanism eliminates the need for manual frequency adjustment by the operator, maintaining simplicity of operation while achieving adaptive efficacy.