Measuring concept semantic relatedness through common spatial pattern feature extraction on EEG signals

We study the semantic relationship between pairs of nouns of concrete objects such as “HORSE - SHEEP” and “SWING - MELON” and how this relationship activity is reflected in EEG signals. We collected 18 sets of EEG records; each set containing 150 events of stimulation. In this work we focus on feature extraction algorithms. Particularly, we highlight Common Spatial Pattern (CSP) as a method of feature extraction. Based on these latter, different classifiers were trained in order to associate a set of signals to a previously learned human answer, pertaining to two classes: semantically related, or not semantically related. The results of classification accuracy were evaluated comparing with other four methods of feature extraction, and using classification algorithms from five different families. In all cases, classification accuracy was benefited from using CSP instead of FDTW, LPC, PCA or ICA for feature extraction. Particularly with the combination CSP-Naïve Bayes we obtained the best average precision of 84.63%.