The presence of emboli in the medium cerebral artery may cause severe brain damage or, in the worst case, death. Emboli classification is not a deterministic task
for the lack of universally accepted classification rules. In this work the use of Radial Basis Functions Neural Networks (RBFNN) is proposed to distinguish among solid emboli, liquid emboli, or artifacts, using as indicators a ratio between signal powers and emboli duration. The result of this work demonstrates that, with RBFNN, it is possible to classify emboli with approximately 88% success
