Estimating Brain Load from the EEG

Abstract

Modern work requires cognitively demanding multitasking and the need for sustained vigilance, which may result in work-related stress and may increase the possibility of human error. Objective methods for estimating cognitive overload and mental fatigue of the brain on-line, during work performance, are needed. We present a two-channel electroencephalography (EEG)–based index, theta Fz/alpha Pz ratio, potentially implementable into a compact wearable device. The index reacts to both acute external and cumulative internal load. The index increased with the number of tasks to be performed concurrently (p = 0.004) and with increased time awake, both after normal sleep (p = 0.002) and sleep restriction (p = 0.004). Moreover, the increase of the index was more pronounced in the afternoon after sleep restriction (p = 0.006). As a measure of brain state and its dynamics, the index can be considered equivalent to the heartbeat, an indicator of the cardiovascular state, thus inspiring the name "brainbeat".

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

“…EEG signals are typically in the ten to hundred µV ranges, and classified as delta rhythm (0.1-3.5 Hz), theta rhythm (4-7.5 Hz), alpha rhythm (8-13 Hz), beta rhythm (14-30 Hz), and gamma rhythm (>30 Hz) [36]. EEG analysis has been applied for neuroscience, cognitive science and psychology through studies that show various brain lobes are responsible for specific cognitive activities [37][38][39][40]. For instance, the frontal lobe is highly associated with problem solving, mental flexibility, judgment, creativity, foresightedness and deficiencies; whereas the temporal lobe is primarily responsible for auditory sensation, perception, language comprehension, long-term memory and sexual behavior.…”

The Analysis of Driving Workload and Gamma Waves on Curved Sections in Expressway

“…For instance, this pattern was found in two studies that used continuous interactive control tasks to manipulate workload (Brookings et al, 1996;Fournier et al, 1999) and a study that used an N-back task to vary working memory load (Gevins et al, 1998).With increasing memory load, workload changes are especially apparent at frontal sites (Gevins et al, 1998;Holm et al, 2009). The formula for calculating the workload is also derived from this point.…”

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“…For instance, numerous indices have been created to better exploit the variation in the alpha and beta bands. One of such is the ratio of the theta activity at frontal sites with the alpha activity at parietal sites (Gevins and Smith, 2003;Holm et al, 2009).…”