Enhanced Sparse Representations of Spike Waveforms Obtained by using the Basis Pursuit Approach

Abstract

In the extracellular neural recordings, the spike waveforms formed by the neurons nearby the recording electrode must be sorted according to their morphology. This process is called as spike sorting and it is an important prerequisite in neural decoding algorithms. Low Q-factor wavelet transforms are frequently being used as feature extractors to detect the discriminative patterns between adjacent neurons’ activity. However, the wavelet coefficients are highly sensitive to noise that may occur due to the employed instrumentation system and the local field potentials defined as the total activity of nearby neurons. However, enhanced sparse representations of the spike wave forms, having reduced noise activity, can be attained by using the basis pursuit method that is applied to the tunable Q-factor wavelet transform coefficients. In the tunable Q-factor wavelet transform, the Q-factor of the wavelet filters can be tuned according to the signal of interest with a controllable redundancy. In the proposed study, enhanced sparse representations of the spike waveforms were obtained by using the basis pursuit approach. Later, the energy values of the decomposed subbands were employed as features that can discriminate morphological differences in spike shapes. Finally, the obtained features were fed to k-nearest neighbors and decision trees learning models in an unbiased cross-validation scheme to objectively measure the effect of the enhanced sparsity decomposition. The qualitative and quantitative results show that the enhanced sparsity-based energy features are superior to the traditional low Q-factor based wavelet decomposition in terms of the accuracy metric.