Assessment of Brain Activities to Human Cognitive States

The human electroencephalogram (EEG), first studied by Berger in the 1920’s, represents macroscopic oscillatory and non-oscillatory brain potentials thought to be generated mostly by synchronous post-synaptic currents in large populations of neurons in the cortex. It is a completely non-invasive measurement of brain function by analyzing the scalp electrical activity generated by brain networks that can be applied repeatedly in patients, normal adults, and children with no risks or limitations. For three generations it has been known that abundant information regarding cognitive states such as alertness and arousal is available in EEG recordings. However, relatively little has been done to capture this information in near-real time until the advent of computers fast enough to adequately process the data and signal processing methods capable of extracting the relevant information. For the past thirty years, the dominant analysis method for human cognitive studies has been the averaged evoked response or event-related potential (ERP). Measures of the EEG spectrum have been widely used only to identify stages of sleep. Now that adequate computing power and signal processing algorithms are available, it is of both practical and theoretical interest to know what information about changes in waking human cognitive capacity and behavior is available in complex EEG signals.

1.1.1. Study of the Transient Brain Dynamics

Several groups have attempted to relate brain potentials recorded non-invasively from

performance or subjective rating measures [1-19]. An early effort in this direction, Pfurtscheller and Araniber first reported a method for quantifying the average transient suppression of alpha band (circa 10-Hz) activity following stimulation [3]. In the last decade, researchers studying Pfurtscheller's event-related desynchronization (ERD, spectral amplitude decreases), and event-related synchronization (ERS, spectral amplitude increases) in a variety of narrow frequency bands (4-40 Hz) have reported on their systematic dependencies on task and cognitive state variables as well as on stimulus parameters [4]. For example, Williamson et al. reported that, given a visually presented arithmetic problem to compute mentally, the resulting subject alpha-band ERD resolved only when the calculation was complete [5].

Typically, psychologists calculated averaged Event-Related Potential (ERP) methods by applying simple measures of peak amplitudes and latencies in ERP averages at single scalp channels and focused on the feasibility studies of brain computer interface (BCI) and biofeedback methods in order to choose characters or move a cursor on a computer screen [6-14]. These response averaging, reducing EEG data sets to one or more averaged ERPs, has been the dominant mode of EEG data analysis in cognitive studies for nearly 40 years. The ERP is accomplished by computing averaging epochs (recording periods) of EEG time-locked to repeated occurrences of sensory, cognitive, or motor events [15-18]. Averaged ERPs evoked by brief unattended visual stimuli consist of a sequence of positive and negative peaks that are generally assumed to reflect activity in individual visual cortical processing regions [19]. In this view, response averaging attempts to remove background EEG activity or unrelated noises, whose time course is presumed to be independent of experimental events, as well as artifactual potentials produced by eye and muscle activity, and reflect only activities which are consistently associated with the stimulus processing in a time-locked way.

1.1.2. Monitoring of Human Cognitive State

During the past 10 years, several scientific researches in electrophysiological analysis had been reported to investigate the feasibility of accurately estimating shifts in an operator’s global level of alertness by monitoring the changes in the physiological signals. These methods can be further categorized into two main fields. One focuses on detecting physical changes during drowsiness by image processing techniques, such as average of eye-closure speed, percentage of eye-closure over time, eye tracking as quantization of drowsiness level, driver’s head movements, and steering wheel angle [20-28]. These methods can be further classified as being either direct contact by attaching sensors to the driver’s body or non-contact types by using optical sensors or video cameras to detect vigilance changes and achieve a satisfactory recognition rate. However, these parameters vary in different environmental situations and driving conditions, it would be necessary to devise different detection logic for different types of vehicles. Recently, Van Ordan and et al. further compared these eye-activity based methods to EEG-based methods for alertness estimates in a compensatory visual tracking task [29]. It showed that although these eye-activity variables are well correlated with the subject performance, those eye-activity based methods require a relatively long moving averaged window aiming to track slow changes in vigilance, whereas the EEG-based method can use a shorter moving averaged window to track second-to-second fluctuations in the subject error in a visual compensatory task [40, 49-52].

The other field focuses on measuring physiological changes of drivers, such as heart rate variability (HRV) and electroencephalogram (EEG), as a means of detecting the human cognitive states [30-34]. It has been known that abundant information in

rapid-eye-movement (REM) sleep [36-37]. NREM sleep is further subdivided into stages 1-4.

In the first part of falling into sleep (micro-sleep at NREM), increasing amplitudes of slow alpha waves of the EEG signals were observed with positive correlation at occipital sites (O1 and O2) and negative correlation at central sites (C3 or C4) [38-39]. While approaches based on EEG signals have the advantages for making accurate and quantitative judgments of alertness levels, relatively little information has been captured in real time until signal processing methods and computer power are fast enough to extract the relevant information from the EEG [40]. Thus, it is practicable and appealing to know what information about human cognitive state and behavior are available through analyzing complex EEG signals.