title: Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.
abstract: This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College, London, SW7 2BT, UK; Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College, London, SW7 2AZ, UK. Electronic address: s.paraskevopoulou09@imperial.ac.uk.
authors.forename: Sivylla E
authors.initials: SE
authors.lastname: Paraskevopoulou
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College, London, SW7 2BT, UK.
authors.forename: Di
authors.initials: D
authors.lastname: Wu
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College, London, SW7 2BT, UK; Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College, London, SW7 2AZ, UK. Electronic address: a.eftekhar@imperial.ac.uk.
authors.forename: Amir
authors.initials: A
authors.lastname: Eftekhar
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College, London, SW7 2BT, UK; Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College, London, SW7 2AZ, UK. Electronic address: t.constandinou@imperial.ac.uk.
authors.forename: Timothy G
authors.initials: TG
authors.lastname: Constandinou
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001479:Basal Ganglia / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology; D016000:Cluster Analysis; D003198:Computer Simulation; D003201:Computers; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008251:Macaca; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted
keywords: Brain machine interface; Clustering; Hardware implementable; Realtime; Spike sorting; Streaming