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Classification of auditory selective attention using spatial coherence and modular attention index.

Ana Paula de Souza1, Quenaz B Soares2, Leonardo B Felix2

  • 1Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Av. Presidente Antônio Carlos, 6627, Pampulha, Belo Horizonte, MG 31270-901, Brazil; Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa/Campus Florestal, Rodovia LMG 818 - km 6, Florestal, MG 35690-000, Brazil; Núcleo Interdisciplinar de Análise de Sinais, Departamento de Engenharia Elétrica, Universidade Federal de Viçosa, Av. Peter Henry Rolfs s/n, Viçosa, MG 36570-900, Brazil.

Computer Methods and Programs in Biomedicine
|November 13, 2018
PubMed
Summary

A new modular classifier significantly improves auditory Brain-Computer Interfaces (BCIs) for paralyzed users. This advanced method enhances selective attention detection using electroencephalogram signals, achieving high accuracy.

Keywords:
Auditory brain-computer interfaceElectroencephalogramSelective attentionSpatial coherence

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Auditory Brain-Computer Interfaces (BCIs) offer a non-muscular, non-gaze communication method for severely paralyzed individuals.
  • Selective attention is a natural human ability leveraged for BCIs, with techniques like Spatial Coherence (SC) enhancing performance.

Purpose of the Study:

  • To investigate and compare two spatial coherence detection strategies for auditory BCIs: contralateral and a novel modular classifier.
  • To optimize auditory BCIs using a new modular attention index classifier for binary choice tasks.

Main Methods:

  • Applied contralateral and modular classifiers to electroencephalogram (EEG) data from 144 subjects.
  • Investigated optimal stimulus carriers, EEG channels, and signal trials to maximize hit rate and information transfer rate.

Main Results:

  • The modular classifier achieved a 91.67% hit rate and 6.74 bits/min information transfer rate.
  • Optimal performance utilized 0.5 kHz/4.0 kHz stimuli, three EEG signal windows, and five specific electrodes (C3, P3, F8, P4, O2).

Conclusions:

  • The modular classifier, utilizing EEG channels from central, frontal, occipital, and parietal areas, enhances auditory BCIs.
  • This approach improves the performance of BCIs based on selective auditory attention.