Classifying ECoG Based Mental Tasks Using Wavelet Transform Features

AYDEMİR Ö., KAYIKÇIOĞLU T.

33rd International Conference on Telecommunication and Signal Processing, Vienna, Avusturya, 17 - 20 Ağustos 2010, ss.103-107 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Basıldığı Şehir: Vienna
  • Basıldığı Ülke: Avusturya
  • Sayfa Sayıları: ss.103-107
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

The input signals of brain computer interfaces may tie either electroencephalogram (EEG) recorded from scalp or electrocorticogram (ECoG) recorded with subdural electrodes. It is very important that the classifiers have the ability for discriminating signals which are recorded in different sessions to make brain computer interfaces practical in use. This paper proposes an algorithm for classifying motor imagery ECoG signals, recorded In different sessions. Extracted feature vectors obtained with wavelet transform were classified by using k nearest neighbor and support vector machine algorithms. The proposed algorithm was successfully applied to Data Set I of BCI competition 2005, and achieved a classification accuracy of 94 % on test set.