Evolution of waveform characteristics in motor imagery among healthy individuals

Motor imagery affects brain activity patterns across frequencies, yet most studies primarily focused on power changes, overlooking intrinsic oscillatory characteristics including waveform nonlinearity and sharpness. To this end, we introduced ensemble empirical mode decomposition (EEMD) to access evolving waveforms, preserving the temporal features of the raw signal across scales. This study used EEG data collected from 20 healthy participants over three consecutive days, randomly assigned to real or sham neurofeedback groups. Each participant completed multiple sessions with or without motor imagery training, and the real-time feedback was based on beta burst detection over the contralateral motor cortex (C3/C4) in the neurofeedback phase. We demonstrated the superiority of EEMD in preserving evolving waveforms of decompositions compared to traditional methods, and systematically compared the degree of nonlinearity, sharpness, and averaged power across frequency bands in motor imagery tasks for healthy individuals. Following neurofeedback training, both the degree of nonlinearity and averaged power in the gamma band exhibited a significant increase, whereas averaged power and sharpness in the low-beta band decreased compared to the no-training condition. The waveform features exhibited an elevated classification performance about power features, improving motor imagery detection accuracies from 76.7% to 82.0%. These findings suggest the significance of waveform characteristics as useful biomarkers alongside average power in identifying motor imagery engagement. The proposed method provides theoretical support for the potential application in motor imagery-related rehabilitation.