We investigated the possibility of using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener’s subjective experience of music into scores that can be used in popular on-demand music streaming services.
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.
Applications of clustering algorithms in biomedical research are ubiquitous. However, due to the diversity of cluster analysis, the diversity of critical elements underlying different clustering algorithms can be daunting.
Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrode’s vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. Read the rest of this entry…
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tCS brain to brain interface Trajectories Plasticity CPG Biometrics Retinal prosthesis neuroethics Graph Theory Exoskeleton music recommendation Connectomics systems-neuroscience music human to human interface transcranial direct current stimulation Deep Brain Stimulation Neural coding Neuroimaging TMS Neuro-inspired Computing Brain-inspired Computing LFP Noise Sparse neurons Wavelets Computational Neuroscience neuroprosthetics EEG Bionics Machine Learning neural recordings Information Theory Dimensionality Reduction Neural Networks Clustering Modelling Statistical Analysis Neurophysiology Spike Sorting Brain Research Brain Interfaces
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