A fully automated method for segmentation and classification of local field potential recordings. Preliminary results.

Identification of brain states measured with electrophysiological methods such as electroencephalography and local field potential (LFP) recordings is of great importance in numerous neuroscientific applications. For instance, in Brain Computer Interface, in the diagnosis of neurological disorders as well as to investigate how brain rhythms stem from synchronized physiological mechanisms (e.g., memory and learning). In this work, we propose a fully automated method with the aim of partitioning LFP signals into stationary segments as well as classifying each detected segment into three different classes (delta, regular theta or irregular theta rhythms). Our approach is computationally efficient since the process of detection and partition of signals into stationary segments is only based on two features (the variance and the so-called spectral error measure) and allow the classification at the same time. We developed the algorithm upon analyzing six anesthetized rats, resulting in a true positive rate of 97.5%, 91.8% and 79.1% in detecting delta, irregular theta and regular theta rhythms, respectively. This preliminary quantitative evaluation offers encouraging results for further research.