title: Frequency Band Separability Feature Extraction Method With Weighted Haar Wavelet Implementation for Implantable Spike Sorting.
abstract: Hardware-efficient feature extraction is an important step for real-time and on-chip spike sorting. Based on an analysis of spike energy spectrum, a new feature set is developed using the positive and negative spike peaks in low and high frequency bands. A separability metric that evaluates the informativeness and noise sensitivity of features is introduced to optimize the cutoff frequency of each band. Haar-based discrete wavelet transform was chosen to implement memory- and hardware-efficient filters for extracting frequency band separability features. Specifically, peaks from the first level detail and the fourth level approximation were used to represent a spike. To improve clustering performance, the detail features were weighted into the same dynamic range as the approximation features. The new feature extraction method was tested at different signal-to-noise ratios using synthesized datasets consisting of considerable and various spike shapes extracted from real neural recordings. The results show that the new method has 3%-10% better spike sorting performance than other hardware-efficient methods while consuming comparable hardware resources.
authors.forename: Yuning
authors.initials: Y
authors.lastname: Yang
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Mason
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis