  In clinical practice, electroencephalography (EEG) plays an important role in
the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can
greatly improve the ac-curacy of epilepsy detection while reducing the workload
of physicians. However, there are many challenges in practical applications for
personalized epileptic EEG detection (i.e., training of detection model for a
specific person), including the difficulty in extracting effective features
from one single view, the undesirable but common scenario of lacking sufficient
training data in practice, and the no guarantee of identically distributed
training and test data. To solve these problems, we propose a TSK fuzzy
system-based epilepsy detection algorithm that integrates multi-view
collaborative transfer learning. To address the challenge due to the limitation
of single-view features, multi-view learning ensures the diversity of features
by extracting them from different views. The lack of training data for building
a personalized detection model is tackled by leveraging the knowledge from the
source domain (reference scene) to enhance the performance of the target domain
(current scene of interest), where mismatch of data distributions between the
two domains is resolved with adaption technique based on maximum mean
discrepancy. Notably, the transfer learning and multi-view feature extraction
are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy
system equip the model with strong fuzzy logic inference capability. Hence, the
proposed method has the potential to detect epileptic EEG signals effectively,
which is demonstrated with the positive results from a large number of
experiments on the CHB-MIT dataset.
