Technology-Aware Algorithm Design for Neural Spike Detection, Feature Extraction, and Dimensionality Reduction

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 18; no. 5; pp. 469 - 478
• Main Authors: Gibson, Sarah, Judy, Jack W., Markovic, Dejan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2010
• Subjects: Action Potentials - physiology; Algorithm design and analysis; Algorithms; Biomedical signal detection; Biomedical signal processing; Brain - physiology; brain-machine interface (BMI); Data communication; dimensionality reduction; Electroencephalography - methods; Evoked Potentials - physiology; Feature extraction; Hardware; Humans; Lifting equipment; Nerve Net - physiology; neural signal processing; Neurons; Neurons - physiology; Pattern Recognition, Automated - methods; Permission; Signal processing algorithms; Sorting; spike detection; spike sorting
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2010.2051683

Applications such as brain-machine interfaces require hardware spike sorting in order to 1) obtain single-unit activity and 2) perform data reduction for wireless data transmission. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection, feature-extraction, and dimensionality-reduction algorithms for spike sorting are described and evaluated in terms of accuracy versus complexity. The nonlinear energy operator is chosen as the optimal spike-detection algorithm, being most robust over noise and relatively simple. Discrete derivatives is chosen as the optimal feature-extraction method, maintaining high accuracy across signal-to-noise ratios with a complexity orders of magnitude less than that of traditional methods such as principal-component analysis. We introduce the maximum-difference algorithm, which is shown to be the best dimensionality-reduction method for hardware spike sorting.