Changes in cortical activity during working memory tasks were examined with electroencephalograms (EEGs) sampled from 115 channels and spatially sharpened with magnetic resonance imaging (MRI)-based finite element deblurring. Eight subjects performed tasks requiring comparison of each stimulus to a preceding one on verbal or spatial attributes. A frontal midline theta rhythm increased in magnitude with increased memory load. Dipole models localized this signal to the region of the anterior cingulate cortex. A slow (low-frequency), parietocentral, alpha signal decreased with increased working memory load. These signals were insensitive to the type of stimulus attribute being processed. A faster (higher-frequency), occipitoparietal, alpha signal was relatively attenuated in the spatial version of the task, especially over the posterior right hemisphere. Theta and alpha signals increased, and overt performance improved, after practice on the tasks. Increases in theta with both increased task difficulty and with practice suggests that focusing attention required more effort after an extended test session. Decreased alpha in the difficult tasks indicates that this signal is inversely related to the amount of cortical resources allocated to task performance. Practice-related increases in alpha suggest that fewer cortical resources are required after skill development. These results serve: (i) to dissociate the effects of task difficulty and practice; (ii) to differentiate the involvement of posterior cortex in spatial versus verbal tasks; (iii) to localize frontal midline theta to the anteromedial cortex; and (iv) to demonstrate the feasibility of using anatomical MRIs to remove the blurring effect of the skull and scalp from the ongoing EEG. The results are discussed with respect to those obtained in a prior study of transient evoked potentials during working memory.

We assessed working memory load during computer use with neural network pattern recognition applied to EEG spectral features. Eight participants performed high-, moderate-, and low-load working memory tasks. Frontal theta EEG activity increased and alpha activity decreased with increasing load. These changes probably reflect task difficulty-related increases in mental effort and the proportion of cortical resources allocated to task performance. In network analyses, test data segments from high and low load levels were discriminated with better than 95% accuracy. More than 80% of test data segments associated with a moderate load could be discriminated from high- or low-load data segments. Statistically significant classification was also achieved when applying networks trained with data from one day to data from another day, when applying networks trained with data from one task to data from another task, and when applying networks trained with data from a group of participants to data from new participants. These results support the feasibility of using EEG-based methods for monitoring cognitive load during human-computer interaction.

The capacity to deliberately control attention in order to hold and manipulate information in working memory is critical to higher cognitive functions. This suggests that between-subject differences in general cognitive ability might be related to observable differences in the activity of brain systems that support working memory and attention control. To test this notion, electroencephalograms were recorded from 80 healthy young adults during spatial working memory tasks. Measures of task-related neurophysiological and behavioral variables were derived from these data and compared to scores on a test battery commonly used to assess general cognitive ability (the WAIS-R). Subjects who scored high on the psychometric test also tended to respond faster in the experimental tasks without any loss of accuracy. The amplitude of the late positive component of the event-related potential was larger in high-ability subjects, and the frontal midline theta component of the EEG signal was also selectively enhanced in this group under conditions of sustained performance and high working memory load. These results suggest that subjects who scored high on the WAIS-R were better able to focus and sustain attention to task performance. Changes in the EEG alpha rhythm in response to manipulations of task practice and load were also examined and compared between frontal and parietal regions. The results indicated that high-ability subjects developed strategies that made relatively greater use of parietal regions, whereas low-ability subjects relied more exclusively on frontal regions. Other analyses indicated that hemispheric asymmetries in alpha band measures distinguish between individuals with relatively high verbal aptitude and those with relatively high nonverbal aptitude. In particular, subjects with a verbal cognitive style tended to make greater use of the left parietal region during task performance, and subjects with a nonverbal style tended to make greater use of the right parietal region. These results help clarify relationships between task-related brain activity and individual differences in cognitive ability and style.

Kushida CA; Nichols DA; Holmes TH; Quan SF; Walsh JK; Gottlieb DJ; Simon RD; Guilleminault C; White DP; Goodwin JL; Schweitzer PK; Leary EB; Hyde PR; Hirshkowitz M; Green S; McEvoy LK; Chan C; Gevins A; Kay GG; Bloch DA; Crabtree T; Demen WC. Effects of continuous positive airway pressure on neurocognitive function in obstructive sleep apnea patients: the Apnea Positive Pressure Long-term Efficacy Study (APPLES). SLEEP 2012;35(12):1593-1602.

Perhaps the most basic issue in the study of cognitive workload is the problem of how to actually measure it. The electroencephalogram (EEG) continues to be the clinical method of choice for monitoring brain function in assessing sleep disorders, level of anaesthesia and epilepsy. This preference reflects the EEG's high sensitivity to variations in alertness and attention, the unimposing conditions under which it can be recorded, and the low cost of the technology it requires. These characteristics also suggest that EEG-based monitoring methods might provide a useful tool in ergonomics. This paper reviews a long-term programme of research aimed at developing cognitive workload monitoring methods based on EEG measures. This research programme began with basic studies of the way neuroelectric signals change in response to highly controlled variations in task demands. The results yielded from such studies provided a basis on which to develop appropriate signal processing methodologies to automatically differentiate mental effort-related changes in brain activity from artifactual contaminants and for gauging relative magnitudes of mental effort in different task conditions. These methods were then evaluated in the context of more naturalistic computerbased work. The results obtained from these studies provide initial evidence for the scientific and technical feasibility of using EEG-based methods for monitoring cognitive load during human-computer interaction.