Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform

Colin M. McCrimmon, Jonathan Lee Fu, Ming Wang, Lucas Silva Lopes, Po T. Wang, Alireza Karimi-Bidhendi, Charles Y. Liu, Payam Heydari, Zoran Nenadic, An Hong Do

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Objective: Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI. Methods: The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-To-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states. Results: The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated (ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 m 0.12 and 0.68 m 0.10, respectively, and were not significantly different. Conclusion: The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. Significance: Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.

Original languageEnglish (US)
Article number7851005
Pages (from-to)2313-2320
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number10
DOIs
StatePublished - Oct 2017

Keywords

  • Biomedical amplifiers
  • brain-computer interfaces
  • embedded software
  • microcontrollers
  • mobile computing
  • neurofeedback

ASJC Scopus subject areas

  • Biomedical Engineering

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