The decision about the actual number of active neurons is an open issue in spike sorting, with sparsely firing neurons and background activity the most influencing factors. Dominant-sets clustering algorithm is a graph-theoretical algorithmic procedure that successfully addresses this issue. The quality of grouping in the data is evaluated with the estimation of ‘cohesiveness’, i.e. a cluster-quality measure, for each group.
Background noise and spike overlap pose problems in contemporary spike-sorting strategies. The (non-linear) isometric feature mapping (ISOMAP) technique reveals the intrinsic data structure and helps with recognising the involved neurons.
To reproduce this tutorial in MATLAB you will need : Read the rest of this entry…
NeuroRighter is an open-source system for multi-electrode closed-loop recording and stimulation originally produced by the Potter Lab at Georgia Tech. The software is free, and the hardware schematics and layouts are free for download. Read the rest of this entry…
Advances in brain-computer interfaces, restorative neurotechnology, and assistive robot technology reported in Nature by the BrainGate2 collaboration of researchers, allowed two people with tetraplegia to reach for and grasp objects in three-dimensional space using robotic arms that they controlled directly with brain activity.
Biometrics Computational Neuroscience CPG LFP Noise Machine Learning Sparse neurons Wavelets neural recordings Information Theory Modelling Dimensionality Reduction Neural Networks Clustering Statistical Analysis Neurophysiology Brain Interfaces Spike Sorting Brain Research
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