title: Computationally efficient feature denoising filter and selection of optimal features for noise insensitive spike sorting.
abstract: Feature extraction is a critical step in real-time spike sorting after a spike is detected. Features should be informative and noise insensitive for high classification accuracy. This paper describes a new feature extraction method that utilizes a feature denoising filter to improve noise immunity while preserving spike information. Six features were extracted from filtered spikes, including a newly developed feature, and a separability index was applied to select optimal features. Using a set of the three highest-performing features, which includes the new feature, this method can achieve spike classification error as low as 5% for the worst case noise level of 0.2. The computational complexity is only 11% of principle component analysis method and it only costs nine registers per channel.
authors.forename: Yuning
authors.initials: Y
authors.lastname: Yang
authors.forename: Samuel
authors.initials: S
authors.lastname: Boling
authors.forename: Amir
authors.initials: A
authors.lastname: Eftekhar
authors.forename: Sivylla E
authors.initials: SE
authors.lastname: Paraskevopoulou
authors.forename: Timothy G
authors.initials: TG
authors.lastname: Constandinou
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Mason
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D009622:Noise; D025341:Principal Component Analysis