Online prediction of self-paced hand-movements from subthalamic activity using neural networks in Parkinson's disease

The significance of local field potential (LFP) activity in the subthalamic nucleus (STN) of patients with Parkinson's disease is unclear. Here we show that it is possible to predict self-paced hand-movements from the oscillatory nature of the STN LFP on a trial-by-trial basis. To this end we used a neural networks' classification algorithm on features representing different measures of spectral activity. Our experiments were simulated to process online LFP signal recordings collected beforehand from macroelectrodes implanted in STN. With spectral features extracted via wavelet transformation, we were able to predict a voluntary hand-movement's onset with 95% sensitivity and 77% specificity. Most predictions were made over a second in advance of the movement. We conclude that oscillatory LFP activity in STN is directly or indirectly related to processes involved in motor preparation. The ability to predict movement in real-time may open up several experimental and therapeutic possibilities.