A Study for the Realization of Online Magnetoencephalography using the Spatio-Spectral Decomposition Algorithms

Neurofeedback systems have been found to be effective in the clinical rehabilitation of paralysis. However, most systems exist only for use with electroencephalography, which is cumbersome to apply to patients and has lower spatial resolution than Magnetoencephalography (MEG). Furthermore, the best practices for neural data feature extraction and feature selection are not well established. The inclusion of the best performing feature extraction algorithms is critical to the development of clinical neurofeedback systems. Using simultaneously collected MEG and accelerometer data before and during 10 spontaneous finger movements, we performed an in-depth comparison of the Spatio-Spectral Decomposition (SSD) algorithms for their individual abilities to isolate movement-relevant features in brain activity. Having restricted raw data to that from sensorimotor rhythm frequencies in select MEG sensors over sensorimotor cortex, we compared SSD components using: (1) 2D topographies, (2) activations over time, (3) and correlations with accelerometer data at both 0 and 60 ms time delays. We will discuss these results and suggestions for application to neurofeedback systems. In particular, we will present detailed visualizations of SSD results and discuss potential strategies and pitfalls for feature selection.