title: Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand.
abstract: OBJECTIVE
  This study aimed to find effective approaches to electroencephalographic (EEG) signal analysis and resolve problems of real and imaginary finger movement pattern recognition and categorization for one hand.
  
  
  METHODS
  Eight right-handed subjects (mean age 32.8 [SD=3.3] years) participated in the study, and activity from sensorimotor zones (central and contralateral to the movements/imagery) was recorded for EEG data analysis. In our study, we explored the decoding accuracy of EEG signals using real and imagined finger (thumb/index of one hand) movements using artificial neural network (ANN) and support vector machine (SVM) algorithms for future brain-computer interface (BCI) applications.
  
  
  RESULTS
  The decoding accuracy of the SVM based on a Gaussian radial basis function linearly increased with each trial accumulation (mean: 45%, max: 62% with 20 trial summarizations), and the decoding accuracy of the ANN was higher when single-trial discrimination was applied (mean: 38%, max: 42%). The chosen approaches of EEG signal discrimination demonstrated differential sensitivity to data accumulation. Additionally, the time responses varied across subjects and inside sessions but did not influence the discrimination accuracy of the algorithms.
  
  
  CONCLUSIONS
  This work supports the feasibility of the approach, which is presumed suitable for one-hand finger movement (real and imaginary) decoding. These results could be applied in the elaboration of multiclass BCI systems.
authors.affiliation: System Analysis and Control department, Saint-Petersburg State Polytechnic University, Politekhnicheskaya 29, St. Petersburg 195251, Russia. Electronic address: sonkink@gmail.com.
authors.forename: Konstantin M
authors.initials: KM
authors.lastname: Sonkin
authors.affiliation: System Analysis and Control department, Saint-Petersburg State Polytechnic University, Politekhnicheskaya 29, St. Petersburg 195251, Russia.
authors.forename: Lev A
authors.initials: LA
authors.lastname: Stankevich
authors.affiliation: System Analysis and Control department, Saint-Petersburg State Polytechnic University, Politekhnicheskaya 29, St. Petersburg 195251, Russia; Neuroimaging Laboratory, N. P. Bechtereva Institute of the Human Brain Russian Academy of Sciences, acad. Pavlova 9, St. Petersburg 197376, Russia.
authors.forename: Julia G
authors.initials: JG
authors.lastname: Khomenko
authors.affiliation: Psychology Department, Saint-Petersburg Institute of International Economic Relations, Economics and Law, pr. Liteinii 42, St. Petersburg 191014, Russia; Laboratory of Comparative Ecological and Physiological Studies, Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, pr. Toreza 44, St. Petersburg 194223, Russia.
authors.forename: Zhanna V
authors.initials: ZV
authors.lastname: Nagornova
authors.affiliation: Psychology Department, Saint-Petersburg Institute of International Economic Relations, Economics and Law, pr. Liteinii 42, St. Petersburg 191014, Russia; Laboratory of Comparative Ecological and Physiological Studies, Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, pr. Toreza 44, St. Petersburg 194223, Russia.
authors.forename: Natalia V
authors.initials: NV
authors.lastname: Shemyakina
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005260:Female; D005385:Fingers; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D009068:Movement; D016571:Neural Networks, Computer; D060388:Support Vector Machine; D013933:Thumb
keywords: Artificial neural network; Brain–computer interface; Electroencephalography; Finger movements; Motor imagery; Support vector machine; Symbolic regression