A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals

Ergun E., Aydemir Ö.

COGNITIVE NEURODYNAMICS, cilt.14, sa.5, ss.609-617, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 5
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s11571-020-09592-8
  • Dergi Adı: COGNITIVE NEURODYNAMICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, Psycinfo
  • Sayfa Sayıları: ss.609-617
  • Anahtar Kelimeler: Brain computer interface, Electroencephalography, Preprocessing, Evolutionary approach, Fusion method, Feature extraction, Classification, PERFORMANCE, ARTIFACTS, REMOVAL
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Brain computer interface systems decode brain activities from electroencephalogram (EEG) signals and translate the user's intentions into commands to control and/or communicate with augmentative or assistive devices without activating any muscle or peripheral nerve. In this paper, we aimed to improve the accuracy of these systems using improved EEG signal processing techniques through a novel evolutionary approach (fusion-based preprocessing method). This approach was inspired by chromosomal crossover, which is the transfer of genetic material between homologous chromosomes. In this study, the proposed fusion-based preprocessing method was applied to an open access dataset collected from 29 subjects. Then, features were extracted by the autoregressive model and classified by k-nearest neighbor classifier. We achieved classification accuracy (CA) ranging from 67.57 to 99.70% for the detection of binary mental arithmetic (MA) based EEG signals. In addition to obtaining an average CA of 88.71%, 93.10% of the subjects showed performance improvement using the fusion-based preprocessing method. Furthermore, we compared the proposed study with the common average reference (CAR) method and without applying any preprocessing method. The achieved results showed that the proposed method provided 3.91% and 2.75% better CA then the CAR and without applying any preprocessing method, respectively. The results also prove that the proposed evolutionary preprocessing approach has great potential to classify the EEG signals recorded during MA task.