Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Ali Maleki; Maedeh Azadimoghadam
Volume 16, Issue 3 , December 2022, , Pages 229-240
Abstract
A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. ...
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A significant challenge in moving SSVEP-based BCIs from the laboratory into real-life applications is that the user may suffer from fatigue. Prolonged execution of commands in a BCI system can cause mental fatigue and, as a result, create dissatisfaction in the user and reduce the system's efficiency. The first step to studying and ultimately reducing the destructive effects of fatigue is to identify the level of fatigue. Although frequency indices have been used for fatigue evaluation, the results of previous research in this field are inconsistent. Therefore, there is no detailed and comprehensive investigation of how fatigue affects frequency indices. In this paper, the evaluation of frequency-domain fatigue indicators has been done accurately and comprehensively. For this purpose, nine visual stimuli with different flickering frequencies were displayed to the subject, and they were asked to pay attention to the target cue. The visual stimulation was presented continuously, without rest to ensure that the fatigue occurs at the end of the test. Mean amplitude of theta, alpha, and beta bands, and 4-30Hz frequency band segments with 1Hz, 2Hz, and 4Hz steps were evaluated as fatigue indices. The results show that the mean amplitude of the frequency band of 8-9 Hz is more suitable for fatigue evaluation. This index has the most changes with fatigue in a state of wakeful relaxation of the subject and the mental effort to maintain the level of alertness in the fatigue state.
