A fast and efficient algorithm for multi-channel transcranial magnetic stimulation (TMS) signal denoising

Area of Science:

• Biomedical Engineering
• Signal Processing
• Neuroscience Instrumentation

Background:

• High-performance magnetic field detection systems, particularly those utilizing 264-channel Transcranial Magnetic Stimulation (TMS), require effective signal denoising.
• Noise suppression is critical for obtaining optimal clean signals in TMS applications.

Purpose of the Study:

• To propose and evaluate an improved generalized morphological filtering (IGMF) algorithm based on adaptive framing for efficient TMS signal denoising.
• To enhance the performance and speed of multi-channel TMS signal processing.

Main Methods:

• An adaptive framing algorithm calculates framing points to segment the TMS signal.
• The proposed IGMF algorithm filters individual signal segments.
• Filtered segments are merged to reconstruct the denoised TMS signal.

Main Results:

• The IGMF algorithm demonstrated superior performance in Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) compared to other methods.
• The algorithm's running time was significantly reduced, ranging from 2.88% to 37.87% of that of other algorithms.
• Efficient denoising and fast processing of 264-channel TMS signals were achieved.

Conclusions:

• The proposed IGMF algorithm based on adaptive framing is an effective method for denoising TMS signals.
• The algorithm offers significant advantages in terms of processing speed and accuracy for multi-channel TMS systems.
• This approach contributes to improved signal quality in high-performance magnetic field detection applications.