Adaptive Frame Decomposition in Slow Wave Cortical Responses to Transcranial Magnetic Stimulation

Since the past decades, the application of transcranial magnetic stimulation (TMS) is getting more interest for basic neurophysiological research and possibly diagnostic purposes. Nevertheless, the separation of magnetically from non-magnetically induced brain activity still remains a challenge due to superimposed secondary effects, in particular an auditory evoked potentials. An optimized tight wavelet frames is proposed for the adaptive extraction of discriminant electroencephalographic time-scale features during TMS and an auditory stimulation (AS) paradigm. This study is focused on large-scale features which correspond to slow wave cortical potentials (SCPs). These potentials might reflect thalamocortical dynamics and are frequently used in biofeedback therapies. The proposed methods allows for a robust extraction of slow wave components and separated clearly the TMS from the AS data. It is concluded that this study strongly supports recent suggestions that TMS modulates SCPs, reinforcing the theory that TMS leads to long term changes in the cortical excitability.