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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Mobile devices offer potential for accessible brain-computer interfaces (BCIs).
  • Existing BCI systems often require specialized hardware and complex setups.
  • Online electroencephalography (EEG) signal processing on smartphones remains a challenge.

Purpose of the Study:

  • To develop and validate a modular signal processing and classification application for online EEG analysis on Android devices.
  • To create the Signal Processing and Classification on Android (SCALA) software.
  • To establish a standardized communication interface for SCALA with external hardware and software.

Main Methods:

  • Implemented a multi-app framework integrating stimulus presentation, data acquisition, processing, classification, and feedback delivery.
  • Utilized an open-source signal processing application, SCALA.
  • Validated the system using a well-established auditory selective attention paradigm with 24-channel EEG.

Main Results:

  • SCALA was successfully implemented, demonstrating sufficient temporal precision for audio events.
  • Above-chance classification results were achieved for all participants in the auditory selective attention task.
  • EEG signal quality confirmed auditory evoked potentials and cognitive event-related potentials differentiating task performance.

Conclusions:

  • A fully smartphone-operated, modular closed-loop BCI system has been presented.
  • SCALA can be combined with various EEG amplifiers and adapted for different experimental paradigms.
  • This development facilitates more accessible and versatile BCI applications.