A biophysically inspired microelectrode recording-based model for the subthalamic nucleus activity in Parkinson's disease

Novel signal-dependent filter bank method for identification of multiple basal ganglia nuclei in Parkinsonian patients

Microelectrode recordings are a valuable tool for assisting localization targets during deep brain stimulation procedures in Parkinson's disease neurosurgery. Attempts to automate and standardize this process have been limited by variability in patient neurophysiology and strong dynamics of microelectrode recordings. In this paper, a methodology for the identification of basal ganglia nuclei is presented that is based on a signal-dependent filter bank method using microelectrode recordings. The method is a customized realization of the discrete wavelet transform via the lifting scheme that is optimally tuned by genetic algorithms. Using this method, unique mother wavelet functions that exhibit an adaptable spectrum to the microelectrode recording dynamic are generated. Additionally, by extracting morphological features from the space-transformed microelectrode recording, it is possible to integrate them into three-dimensional (3D) feature spaces with maximum class separability. Finally, high discriminant feature spaces are fed into basic classifiers to recognize up to four basal nuclei. Comparison with several existing wavelets highlights the characteristics of new mother wavelets. Additionally, classification results show that identification of addressed nuclei in the basal ganglia can be performed with 95% confidence.

A Multiscale Correlation of Wavelet Coefficients Approach to Spike Detection

Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of spike detection is to extract action potentials from the recordings, which are often compounded with noise from different sources. This study proposes a new detection algorithm that leverages a technique from wavelet-based image edge detection. It utilizes the correlation between wavelet coefficients at different sampling scales to create a robust spike detector. The algorithm has one tuning parameter, which potentially reduces the subjectivity of detection results. Both artificial benchmark data sets and real neural recordings are used to evaluate the detection performance of the proposed algorithm. Compared with other detection algorithms, the proposed method has a comparable or better detection performance. In this letter, we also demonstrate its potential for real-time implementation.