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Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study

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Abstract

Fatigue deteriorates the performance of a brain-computer interface (BCI) system; thus, reliable detection of fatigue is the first step to counter this problem. The fatigue evaluated by means of electroencephalographic (EEG) signals has been studied in many research projects, but widely different results have been reported. Moreover, there is scant research when considering the fatigue on steady-state visually evoked potential (SSVEP)-based BCI. Therefore, nowadays, fatigue detection is not a completely solved topic. In the current work, the issues found in the literature that led to the differences in the results are identified and saved by performing a new experiment on an SSVEP-based BCI system. The experiment was long enough to produce fatigue in the users, and different SSVEP stimulation ranges were used. Additionally, the EEG features commonly reported in the literature (EEG rhythms powers, SNR, etc.) were calculated as well as newly proposed features (spectral features and Lempel–Ziv complexity). The analysis was carried out on O1, Oz and O2 channels. This work found a tendency of displacement from high-frequency rhythms to low-frequency ones, and thus, better EEG features should present a similar behaviour. Then, the ‘relative power’ of EEG rhythms, the rates (θ + α)/β, α/β and θ/β, some spectral features (central and mean frequencies, asymmetry and kurtosis coefficients, etc.) and Lempel–Ziv complexity are proposed as reliable EEG features for fatigue detection. Hence, this set of features may be used to construct a more trustworthy fatigue index.

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The EEG data is available on request to authors.

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Authors and Affiliations

  1. Instituto de Bioingeniería (INBIO), Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina

    Pablo Diez, Lorena Orosco, Agustina Garcés Correa & Luciano Carmona

  2. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Pablo Diez, Lorena Orosco, Agustina Garcés Correa & Luciano Carmona

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  1. Pablo Diez
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  2. Lorena Orosco
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  3. Agustina Garcés Correa
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  4. Luciano Carmona
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Correspondence to Pablo Diez.

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Diez, P., Orosco, L., Garcés Correa, A. et al. Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study. Med Biol Eng Comput 62, 1475–1490 (2024). https://doi.org/10.1007/s11517-023-03000-z

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