Medical & Biological Engineering & Computing

, Volume 57, Issue 12, pp 2705–2715 | Cite as

A comparison of subject-dependent and subject-independent channel selection strategies for single-trial P300 brain computer interfaces

  • Yanina AtumEmail author
  • Marianela Pacheco
  • Rubén Acevedo
  • Carolina Tabernig
  • José Biurrun Manresa
Original Article


Brain computer interfaces (BCI) represent an alternative for patients whose cognitive functions are preserved, but are unable to communicate via conventional means. A commonly used BCI paradigm is based on the detection of event-related potentials, particularly the P300, immersed in the electroencephalogram (EEG). In order to transfer laboratory-tested BCIs into systems that can be used by at homes, it is relevant to investigate if it is possible to select a limited set of EEG channels that work for most subjects and across different sessions without a significant decrease in performance. In this work, two strategies for channel selection for a single-trial P300 brain computer interface were evaluated and compared. The first strategy was tailored specifically for each subject, whereas the second strategy aimed at finding a subject-independent set of channels. In both strategies, genetic algorithms (GAs) and recursive feature elimination algorithms were used. The classification stage was performed using a linear discriminant. A dataset of EEG recordings from 18 healthy subjects was used test the proposed configurations. Performance indexes were calculated to evaluate the system. Results showed that a fixed subset of four subject-independent EEG channels selected using GA provided the best compromise between BCI setup and single-trial system performance.


Subject-independent channel selection Brain-computer interface P300 Genetic algorithm Recursive feature elimination 


Funding sources

This work was financially supported by the National University of Entre Ríos (UNER), PID No. 6163.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Laboratory for Rehabilitation Engineering and Neuromuscular and Sensory Research (LIRINS), Faculty of EngineeringNational University of Entre Ríos (UNER)Oro VerdeArgentina
  2. 2.Institute for Research and Development in Bioengineering and Bioinformatics (IBB), National Scientific and Technical Research Council, CONICET-UNEROro VerdeArgentina

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