Table 3. Spike sorting results of four software packages, Klusta-Kwik, wave_clus, Osort, and NeuroQuest, with five datasets, four simulated data and one real data.
( ) indicates the number of classified units and TP represents true positive. For NeuroQuest, details of spike sorting methods, selected feature and clustering method, are listed. The number of clusters was decided by the modified EM algorithm (Figueiredo and Jain, 2002).
| SNR4 (11 units) | SNR3 (11 units) | SNR2 (11 units) | Wave_clus data (3 units) | real data (5 units) | |
|---|---|---|---|---|---|
| Klusta Kwik | (11 units) TP: 99.96% | (11 units) TP: 95.32% | (9 units) TP : 86.82% | (1 units) TP : 42.51% | (3 units) TP : 67.43% |
| Wave_clus | (11 units) TP : 99.96% | (10 units) TP : 92.50% | (8 units) TP : 86.48% | (3 units) TP : 98.48% | (4 units) TP : 84.72% |
| Osort | (11 units) TP : 99.84% | (11 units) TP : 93.13% | (9 units) TP : 85.83% | (1 units) TP : 46.72% | (2 units) TP : 41.75% |
| Neuro Quest | (11 units) Temporal PCA Fuzzy c-mean TP : 99.92% |
(11 units) Temporal PCA Fuzzy c-mean TP : 98.84% |
(10 units) Temporal PCA Fuzzy c-mean Class merging Sub-clustering TP : 91.43% |
(2 units) wavelet footprint Fuzzy c-mean TP : 73.24% |
(5 units) Temporal PCA Fuzzy c-mean TP : 98.85% |