University of Michigan

Researchers at the University of Michigan, Ann Arbor have built SpikeCleaner, an algorithm that grades neural units after automated spike sorting, reaching 97% accuracy and a 92% F1 score on single units in benchmarking. It combines spike rate, spike-timing metrics and waveform features to classify units as good, noise or multi-unit activity, a job that otherwise falls to manual curation.

Using implantable brain-machine interface (iBMI) data from a non-human primate two-dimensional finger task, the study tests whether brain-muscle synergies improve decoding performance and generalization. Principal component analysis (PCA), demixed PCA (dPCA) and non-negative matrix factorization (NMF) all compressed brain-muscle data effectively with minimal decoding-accuracy loss, but none improved performance through denoising or enhanced cross-task generalization. The authors conclude that extracting synergies alone does not yield a better or cleaner control space for linear decoding, and call for larger samples and more muscle channels.