Motor Imagery EEG Signals Marginal Time Coherence Analysis for Brain-Computer Interface

The synchronization of neural activity in the human brain has great significance for coordinating its various cognitive functions. It changes throughout time and response to frequency. is measured terms signals, like an electroencephalogram (EEG). time-frequency (TF) among several EEG channels this research using efficient approach. Most frequently, windowed Fourier transforms-short-time transform (STFT), as well wavelet (WT), are used measure TF coherence. information provided by these model-based methods domain insufficient. proposed synchro squeezing (SST)-based representation a data-adaptive approach resolving problem traditional one. enables more perfect estimation better tracking components. SST generates clearly defined depiction because data flexibility frequency reassignment capabilities. Furthermore, non-identical smoothing operator smooth coherence, which enhances statistical consistency synchronization. experiment run both simulated actual data. outcomes show that suggested SST-dependent system performs significantly than previously mentioned approaches. As result, coherences dependent on distinguish between forms motor imagery movement. coherence can be interdependencies activities.