Cognitive performance drop detection during daily activities using EEG

Investing long hours in a cognitively demanding activity without adequate rest has been shown to lead to a decline in cognitive capacity. For this reason, it is crucial to know the moments in which the mental performance is low, to disconnect and recover. This paper presents the design of brain signal processing pipeline using electroencephalographic (EEG) signals to detect cognitive performance drops during sessions that require low physical activity, to determine when users should pause the execution of their current task to take a rest. The developed system is adaptable to any user without requiring prior training. The evaluation considers three mental states: attention, mental fatigue and stress as the most representative; these mental states were re-referenced using the first five minutes of each recording as a calibration period, before applying a set of rules to determine cognitive performance drops. The results showed that, for sixty-two monotonous driving simulation sessions (78.5 ± 22.4 minutes), the detection time occurred at 35.3 ± 18.9 minutes in 80.6% of the sessions, and for three studying sessions (30, 20 and 30 minutes each) the detection time occurred at 11.9, 12.3 and 8.3 minutes, respectively.