Brain–Computer Interface Contributions to Neuroergonomics

Abstract

Brain-Computer Interfaces (BCIs) are systems that can translate brain activity patterns into messages or commands for an interactive application. As such the technology used to design them, and in particular to design passive BCIs which are a new means to perform mental state monitoring, can greatly benefit the neuroergonomics field. Therefore, this chapter describes the classical structure of the brain signal processing chain employed in BCIs, notably presenting the typically used preprocessing (spatial and spectral filtering, artefact removal), feature extraction and classification algorithms. It also gives examples of the use of BCI technology for neuroergonomics applications, either offline for evaluation purposes (e.g. cockpit design or stereoscopic displays' assessment), or online for adaptation purposes (e.g. video game difficulty level or air traffic controller display adaptation).

Monitoring Pilot’s Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions

“…Indeed, mental overload can alter visual [5] and auditory [6,7] attention, working memory abilities [8], the execution of actions [9], and lead to poor decision making [10]. One solution to improve flight safety and mitigate the occurence of human errors is to implement a passive brain computer interface (pBCI) [11,12,13] to continuously monitor mental workload [14] and to dynamically adapt pilot-cockpit interaction [15].…”

A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

“…A relevant approach to improve flight safety is to implement passive brain computer interfaces (pBCI) or neuroadaptive technology [15], [16], [17], [18] to continuously monitor pilots' attentional state and to detect the possible occurrence of degraded states. Recently, [12] implemented an offline pBCI to detect inattentional deafness to auditory alarms during a simulated flight.…”

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

“…The major takeaway of the system is the brain–machine interface (BMI) aspect in the form of a portable PFC fNIRS-based interface with lightweight exoskeletal hand, and limited non-ergonomic advantages are the system's portability, MWL applicability, ease of use, curve shape fitting on PFC, and translating controls to the lightweight exoskeletal hand. Since the main neuroergonomic advantages to the exoskeleton are the human brain at work using MWL, we tried to design the soft exoskeleton hand that may assist a person's daily working, whether in physical grasping or holding tasks (von Lühmann et al, 2015 ; Lotte and Roy, 2018 ). In the case of a stroke patient, when he or she tries to generate operational commands, the neural blood flow and pathways of the patients are different in case of the injury or disease (Birbaumer and Cohen, 2007 ; Chodobski et al, 2011 ; Käthner et al, 2017 ).…”