Decoding Neural Mechanisms of Meditation in Amyotrophic Lateral Sclerosis Patients via Multichannel Electroencephalogram Time-Frequency Analysis

This paper presents an improved algorithmic framework for electroencephalogram characterization aimed at exploring the neural mechanisms in the meditative state of amyotrophic lateral sclerosis patients. The study innovatively combines the adaptive wavelet transform with the Welch spectrum estimation method with dynamic window optimization, thus improving the real-time performance of high-frequency signals with low-frequency signal resolution. The study analyzed the neural mechanisms of the meditative state in amyotrophic lateral sclerosis patients from multiple dimensions, including time domain, frequency domain and time-frequency domain. The results showed that amyotrophic lateral sclerosis patients exhibited specific neural activity patterns such as prefrontal Theta wave inhibition and central area Beta wave modulation during the meditative state. This study provides theoretical support for the neuromodulation of amyotrophic lateral sclerosis patients and the development of brain-computer interface technology.