Technology-aware algorithm design for neural spike detection, feature extraction, and dimensionality reduction.
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.
Gibson
Sarah
S
Department of Electrical Engineering, University of California, Los Angeles, CA 90095, USA. sarah@ee.ucla.edu
Judy
Jack W
JW
Marković
Dejan
D
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*