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".

No abstract

BackgroundLarge amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets.MethodsThe auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks.ResultsThe algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate.ConclusionThe developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

Digital monitoring of physiological signals can allow computer systems to adapt unobtrusively to users, so as to enhance personalised 'smart' interactions. In recent years, physiological computing has grown as a research field, and it is increasingly considered in diverse applications, ranging from specialised work contexts to consumer electronics. Working in this emerging field requires comprehension of several physiological signals, psychophysiological states or 'indices', and analysis techniques. The resulting literature encompasses a complex array of knowledge and techniques, presenting a clear challenge to the practitioner.We provide a foundational review of the field of psychophysiology to serve as a primer for the novice, enabling rapid familiarisation with the core concepts, or as a quick-reference resource for advanced readers. We place special emphasis on everyday human-computer interface applications, drawing a distinction from clinical or sports applications, which are more commonplace. The review provides a framework of commonly understood terms associated with experiential constructs and physiological signals. Then, 12 short and precisely focused review chapters describe 10 individual signals or signal sources and present two technical discussions of online data fusion and processing. A systematic review of multimodal studies is provided in the form of a reference table. We conclude with a general discussion of the application of psychophysiology to human-computer interaction, including guidelines and challenges.

The use of psychophysiologic signals in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of different physiological signals and analysis techniques. Cardiovascular signals such as heart rate variability and blood pressure variability are commonly used in psychophysiology in order to investigate phenomena such as mental workload. In this paper we present a short review of different cardiovascular metrics useful in the context of humancomputer interaction. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We emphasise everyday humancomputer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, with 12 similar chapters, plus application guidelines and systematic review.