Publications & Research

For over a decade, the Advanced Brain Monitoring team has led or partnered in clinical research studies resulting in over 50 publications. In the past year, our strong and growing technical team of PhD- and masters-level hardware engineers, biostatisticians, software engineers and clinical study design experts added three more peer-reviewed findings published in internationally accredited journals to our library. While the study topics and outcomes are wide ranging, the results of each were driven by ABM developed technology including the B-Alert wireless-EEG systems.

Abstract:
Four males and four females operated a 50-min challenging and a 55-min monotonous driving simulator (DS) scenario with randomly presented divided attention tasks (DAT) while fully rested on Days 1 and 2 and partially sleep-deprived on Day 3. Subjective visual analog scales (SS) rating attention, cognition, concentration, sleepiness and stress were administered before and after each DS scenario. One-second epochs of EEG recorded from CzPz and CzOz were classified into high vigilance (HV), low vigilance (LV), EEG associated with eyes closed condition (EC) or sleepy (S) using a multi-level, discriminant function analysis (DFA). Correlations were computed between the DFA classes, DS performance measures and SS responses.

HV was positively correlated with high levels of self-reported attention, cognition, and concentration, and low levels of sleepiness and stress. LV was negatively correlated with high levels of attention, cognition, and concentration, and positively correlated with sleepiness and stress. Epochs classified as EC showed the greatest correlation with driving performance, demonstrated by the positive correlation with accidents (r = 0.44), collisions (r = 0.17), traffic tickets (r = 0.26), and incorrect DAT responses (r = 0.70) and negative correlation with correct DAT responses (r = -0.54). HV was also negatively correlated with traffic tickets (r = -0.19). While attention and number of traffic tickets were negatively correlated (r = -0.22), stress and number of collisions (r = 0.23) and speeding tickets (r = 0.32) were positively correlated. These results demonstrate that EEG acquired while driving can be used to assess levels of alertness, which correspond with performance and self-reported states. Funded by NIH, NINDS grant R44NS 35387 and contract N44NS92367.

Abstract:
Electroencephalographic (EEG) parameters are sensitive indicators of drowsiness and have proven to correlate with performance on a second-by-second basis (Makeig, 1995). Acquisition of high-quality EEG recordings in workplace environments, such as airplane cockpits, long-haul truck cabins and train-operator’s quarters suggest the feasibility of a real-time EEG drowsiness monitor. In this study, a discriminant function analysis (DFA) model designed to classify one-second epochs of EEG on a continuum from highly vigilant to sleep onset was validated. This model utilized methods to overcome between-subject variability in alpha generation as well as distinguish theta activity at sleep onset from frontal midline theta during mental performance tasks (Takahashi 1997).

Abstract:
Electroencephalographic (EEG) parameters are sensitive indicators of drowsiness and have been proven to correlate with performance on a second-by-second basis (Makeig, 1995). This study applies the B-Alert system, a discriminant function analysis (DFA) model designed to classify one-second epochs of EEG on a continuum from highly vigilant to sleep onset to quantify the effects of partial sleep deprivation. The system was designed to provide real-time detection of drowsiness and recommend the optimal time to take a short nap to extend vigilance.

Abstract:
The integration of brain monitoring into the man–machine interface holds great promise for real-time assessment of operator status and intelligent allocation of tasks between machines and humans. This article presents an integrated hardware and software solution for acquisition and real-time analysis of the electroencephalogram (EEG) to monitor indexes of alertness, cognition, and memory. Three experimental paradigms were evaluated in a total of 45 participants to identify EEG indexes associated with changes in cognitive workload: the Warship Commander Task (WCT), a simulated navy command and control environment that allowed workload levels to be systematically manipulated; a cognitive task with three levels of difficulty and consistent sensory inputs and motor outputs; and a multisession image learning and recognition memory test. Across tasks and participants, specific changes in the EEG were identified that were reliably associated with levels of cognitive workload. The EEG indexes were also shown to change as a function of training on the WCT and the learning and memory task. Future applications of the system to augment cognition in military and industrial environments are discussed.