A Comparison of Neural Feature Extraction Methods for Brain-Machine Interfaces

Brain-machine interfaces (BMIs) have shown promise in augmenting people's control of their surroundings, especially for those suffering from paralysis due to neurological disorders. This paper describes an experiment using the rodent model to explore information available in neural signals recorded from chronically implanted intracortical microelectrode arrays. In offline experiments, a number of neural feature extraction methods were utilized to obtain neural activity vectors (NAVs) describing the activity of the underlying neural population while rats performed a discrimination task. The methods evaluated included standard techniques such as binned spike rates and local field potential spectra as well as more novel approaches including matchedfilter energy, raw signal spectra, and an autocorrelation energy measure (AEM) approach. Support vector machines (SVMs) were trained offline to classify left from right going movements by utilizing features contained in the NAVs obtained by the different methods. Each method was evaluated for accuracy and robustness. Results show that most algorithms worked well for decoding neural signals both during and prior to movement, with spectral methods providing the best stability.