Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.

Electroencephalography (EEG) is a non-invasive technique used to record the brain's evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

Promoting safety mindfulness: Recommendations for the design and use of simulation-based training in radiation therapy.

There is a need to better prepare radiation therapy (RT) providers to safely operate within the health information technology (IT) sociotechnical system. Simulation-based training has been preemptively used to yield meaningful improvements during providers' interactions with health IT, including RT settings. Therefore, on the basis of the available literature and our experience, we propose principles for the effective design and use of simulated scenarios and describe a conceptual framework for a debriefing approach to foster successful training that is focused on safety mindfulness during RT professionals' interactions with health IT.

Effects of auditory and tactile warning on response to visual hazards under a noisy environment.

A warning signal presented via a visual or an auditory cue might interfere with auditory or visual information inside and outside a vehicle. On the other hand, such interference would be certainly reduced if a tactile cue is used. Therefore, it is expected that tactile cues would be promising as warning signals, especially in a noisy environment. In order to determine the most suitable modality of cue (warning) to a visual hazard in noisy environments, auditory and tactile cues were examined in this study. The condition of stimulus onset asynchrony (SOA) was set to 0ms, 500ms, and 1000ms. Two types of noises were used: white noise and noise outside a vehicle recorded in a real-world driving environment. The noise level LAeq (equivalent continuous A-weighted sound pressure level) inside the experimental chamber of each type of noise was adjusted to approximately 60 dB (A), 70 dB (A), and 80 dB (A). As a result, it was verified that tactile warning was more effective than auditory warning. When the noise outside a vehicle from a real-driving environment was used as the noise inside the experimental chamber, the reaction time to the auditory warning was not affected by the noise level.

The Work Compatibility Improvement Framework: an integrated perspective of the human-at-work system.

The industrial revolution demonstrated the limitations of a pure mechanistic approach towards work design. Human work is now seen as a complex entity that involves different scientific branches and blurs the line between mental and physical activities. Job design has been a traditional concern of applied psychology, which has provided insight into the interaction between the individual and the work environment. The goal of this paper is to introduce the human-at-work system as a holistic approach to organizational design. It postulates that the well-being of workers and work outcomes are issues that need to be addressed jointly, moving beyond traditional concepts of job satisfaction and work stress. The work compatibility model (WCM) is introduced as an engineering approach that seeks to integrate previous constructs of job and organizational design. The WCM seeks a balance between energy expenditure and replenishment. The implementation of the WCM in industrial settings is described within the context of the Work Compatibility Improvement Framework. A sample review of six models (motivation-hygiene theory; job characteristics theory; person-environment fit; demand-control model; and balance theory) provides the foundation for the interaction between the individual and the work environment. A review of three workload assessment methods (position analysis questionnaire, job task analysis and NASA task load index) gives an example of the foundation for the taxonomy of work environment domains. Previous models have sought to identify a balance state for the human-at-work system. They differentiated between the objective and subjective effects of the environment and the worker. An imbalance between the person and the environment has been proven to increase health risks. The WCM works with a taxonomy of 12 work domains classified in terms of the direct (acting) or indirect (experienced) effect on the worker. In terms of measurement, two quantitative methods are proposed to measure the state of the system. The first method introduced by Abdallah et al. (2004) identifies operating zones. The second method introduced by Salem et al. (2006) identifies the distribution of the work elements on the x/y coordinate plane. While previous efforts have identified some relevant elements of the systems, they failed to provide a holistic, quantitative approach combining organizational and human factors into a common framework. It is postulated that improving the well-being of workers will simultaneously improve organizational outcomes. The WCM moves beyond previous models by providing a hierarchical structure of work domains and a combination of methods to diagnose any organizational setting. The WCM is an attempt to achieve organizational excellence in human resource management, moving beyond job design to an integrated improvement strategy. A joint approach to organizational and job design will not only result in decreased prevalence of health risks, but in enhanced organizational effectiveness as well. The implementation of the WCM, that is, the Work Compatibility Improvement Framework, provides the basis for integrating different elements of the work environment into a single reliable construct. An improvement framework is essential to ensure that the measures of the WCM result in a system that is adaptive and self-regulated.