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".

Despite the significant effects of rTMS on tinnitus, differences between active and placebo groups remained non-significant, due to large placebo-effect and wide inter-individual variation.

We studied the recovery of multitask performance and sleepiness from acute partial sleep deprivation through rest pauses embedded in performance sessions and an 8 h recovery sleep opportunity the following night. Sixteen healthy men, aged 19-22 yrs, participated in normal sleep (two successive nights with 8 h sleep) and sleep debt (one 2 h night sleep followed by an 8 h sleep the following night) conditions. In both conditions, the participants performed four 70 min multitask sessions, with every other one containing a 10 min rest pause with light neck-shoulder exercise. The multitask consisted of four simultaneously active subtasks, with the level of difficulty set in relation to each participant's ability. Physiological sleepiness was assessed with continuous electroencephalography/electro-oculography recordings during themultitask sessions, and subjective sleepiness was self-rated with the Karolinska Sleepiness Scale. Results showed that multitask performance and physiological and subjective sleepiness were impaired by the sleep debt ( p > .001). The rest pause improved performance and subjective sleepiness for about 15 min, regardless of the amount of prior sleep ( p > .01-.05). Following recovery sleep, all outcome measures showed marked improvement ( p < .001), but they failed to reach the levels observed in the control condition ( p < .001-.05). A correlation analysis showed the participants whose multitask performance deteriorated the most following the night of sleep loss tended to be the same persons whose performance was most impaired following the night of the recovery sleep ( p < .001). Taken together, our results suggest that a short rest pause with light exercise is not an effective countermeasure in itself for sleep debt-induced impairments when long-term effects are sought. In addition, it seems that shift arrangements that lead to at least a moderate sleep debt should be followed by more than one recovery night to ensure full recovery. Persons whose cognitive performance is most affected by sleep debt are likely to require the most sleep to recover.

Background Clinical characterization of motion in patients with Parkinson disease (PD) is challenging: symptom progression, suitability of medication, and level of independence in the home environment can vary across time and patients. Appointments at the neurological outpatient clinic provide a limited understanding of the overall situation. In order to follow up these variations, longer-term measurements performed outside of the clinic setting could help optimize and personalize therapies. Several wearable sensors have been used to estimate the severity of symptoms in PD; however, longitudinal recordings, even for a short duration of a few days, are rare. Home recordings have the potential benefit of providing a more thorough and objective follow-up of the disease while providing more information about the possible need to change medications or consider invasive treatments. Objective The primary objective of this study is to collect a dataset for developing methods to detect PD-related symptoms that are visible in walking patterns at home. The movement data are collected continuously and remotely at home during the normal lives of patients with PD as well as controls. The secondary objective is to use the dataset to study whether the registered medication intakes can be identified from the collected movement data by looking for and analyzing short-term changes in walking patterns. Methods This paper described the protocol for an observational case-control study that measures activity using three different devices: (1) a smartphone with a built-in accelerometer, gyroscope, and phone orientation sensor, (2) a Movesense smart sensor to measure movement data from the wrist, and (3) a Forciot smart insole to measure the forces applied on the feet. The measurements are first collected during the appointment at the clinic conducted by a trained clinical physiotherapist. Subsequently, the subjects wear the smartphone at home for 3 consecutive days. Wrist and insole sensors are not used in the home recordings. Results Data collection began in March 2018. Subject recruitment and data collection will continue in spring 2019. The intended sample size was 150 subjects. In 2018, we collected a sample of 103 subjects, 66 of whom were diagnosed with PD. Conclusions This study aims to produce an extensive movement-sensor dataset recorded from patients with PD in various phases of the disease as well as from a group of control subjects for effective and impactful comparison studies. The study also aims to develop data analysis methods to monitor PD symptoms and the effects of medication intake during normal life and outside of the clinic setting. Further applications of these methods may include using them as tools for health care professionals to monitor PD remotely and applying them to other movement disorders. Trial Registration ClinicalTrials.gov NCT03366558; https:/...

The ability of different short-term heart rate variability metrics to classify the level of mental workload (MWL) in 140 s segments was studied. Electrocardiographic data and event related potentials (ERPs), calculated from electroencephalographic data, were collected from 13 healthy subjects during the performance of a computerised cognitive multitask test with different task load levels. The amplitude of the P300 component of the ERPs was used as an objective measure of MWL. Receiver operating characteristics analysis (ROC) showed that the time domain metric of average interbeat interval length was the best-performing metric in terms of classification ability.