Title: Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection,
Abstract: Inter-subject variability between individuals poses a challenge in
inter-subject brain signal analysis problems. A new algorithm for
subject-selection based on clustering covariance matrices on a Riemannian
manifold is proposed. After unsupervised selection of the subsets of relevant
subjects, data in a cluster is mapped to a tangent space at the mean point of
covariance matrices in that cluster and an SVM classifier on labeled data from
relevant subjects is trained. Experiment on an EEG seizure database shows that
the proposed method increases the accuracy over state-of-the-art from 86.83% to
89.84% and specificity from 87.38% to 89.64% while reducing the false positive
rate/hour from 0.8/hour to 0.77/hour.