BioMedical Engineering OnLine · 2014 · 188 citations · 27 references
EngineeringComputer EngineeringNeural EngineeringPsychophysiologyTime Series AnalysisBrain-computer InterfacesElectroencephalogram AnalysisElectroencephalogram Signal ProcessingElectroencephalographyEvoked PotentialsVisual Evoked PotentialsBrain-computer Interface Performance EvaluationElectroencephalography-based Brain-computer InterfacesFatigue AnalysisFatigue MeasurementSignal-to-noise RatioSignal-to-noise Ratio AnalysisSteady-state Visual Evoked Potential InterfacesVisual Evoked Potential Analysis
BACKGROUND: The fatigue that users suffer when using steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can cause a number of serious problems such as signal quality degradation and system performance deterioration, users' discomfort and even risk of photosensitive epileptic seizures, posing heavy restrictions on the applications of SSVEP-based BCIs. Towards alleviating the fatigue, a fundamental step is to measure and evaluate it but most existing works adopt self-reported questionnaire methods which are subjective, offline and memory dependent. This paper proposes an objective and real-time approach based on electroencephalography (EEG) spectral analysis to evaluate the fatigue in SSVEP-based BCIs. METHODS: How the EEG indices (amplitudes in δ, θ, α and β frequency bands), the selected ratio indices (θ/α and (θ + α)/β), and SSVEP properties (amplitude and signal-to-noise ratio (SNR)) changes with the increasing fatigue level are investigated through two elaborate SSVEP-based BCI experiments, one validates mainly the effectiveness and another considers more practical situations. Meanwhile, a self-reported fatigue questionnaire is used to provide a subjective reference. ANOVA is employed to test the significance of the difference between the alert state and the fatigue state for each index. RESULTS: Consistent results are obtained in two experiments: the significant increases in α and (θ + α)/β, as well as the decrease in θ/α are found associated with the increasing fatigue level, indicating that EEG spectral analysis can provide robust objective evaluation of the fatigue in SSVEP-based BCIs. Moreover, the results show that the amplitude and SNR of the elicited SSVEP are significantly affected by users' fatigue. CONCLUSIONS: The experiment results demonstrate the feasibility and effectiveness of the proposed method as an objective and real-time evaluation of the fatigue in SSVEP-based BCIs. This method would be helpful in understanding the fatigue problem and optimizing the system design to alleviate the fatigue in SSVEP-based BCIs.
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Driver fatigue: Electroencephalography and psychological assessment
Ashley Craig · Psychophysiology · 2002 · 530 citations