scholarly journals Mental Workload Estimation Using Wireless EEG Signals

2019 ◽  
Author(s):  
Quadri Adewale ◽  
George Panoutsos
Keyword(s):  
Real Time ◽  
Feature Matching ◽  
Mental Arithmetic ◽  
Mental Workload ◽  
Time Estimation ◽  
Arithmetic Task ◽  
Mental Arithmetic Task ◽  
Real Time Estimation ◽  
Wireless Eeg ◽  
Back Task

AbstractPrevious studies have shown that electroencephalogram (EEG) can be used in estimating mental workload. However, developing fast and reliable models for cross-task, cross-subject and cross-session classifications of workload remains a challenge. In this study, a wireless Emotiv EPOC headset was used to evaluate workload in two different mental tasks: n-back task and mental arithmetic task. 0-back task and 2-back task were employed as low and high workload in the n-back task while 1-digit and 3-digit addition were used as the two different workload levels in the arithmetic task. Using power spectral density as features, a fast signal processing and feature extraction framework was developed to facilitate real-time estimation of workload. Within-session accuracies of 98.5% and 95.5% were achieved in the n-back and arithmetic tasks respectively. Adaptive subspace feature matching (ASFM) was applied for cross-session, cross-task and cross-subject classifications. The feature adaptation provided average cross-session accuracies of 80.5% and 74.4% in the n-back and the arithmetic tasks respectively. An average cross-task accuracy of 68.6% was achieved while cross-subject accuracies were 74.4% and 64.1% in the n-back and arithmetic tasks respectively. The framework generalised well across subjects and tasks, and it provided a promising approach towards developing subject and task-independent models. This study also shows that a consumer-level wireless EEG headset can be applied in cognitive monitoring for real-time estimation of workload in practice.

scholarly journals 2C3-3　Real-Time Estimation of Mental-WorkLoad for Drivers Based on Multidimensional Biosignal Analysis

2019 ◽  
Vol 55 (Supplement) ◽  
pp. 2C3-3-2C3-3
Author(s):  
Takeshi SHIMIZU ◽  
Takayuki MUKAEDA ◽  
Keisuke SHIMA ◽  
Syu MURAJI ◽  
Juntaro MATSUO ◽  
...  
Keyword(s):  
Real Time ◽  
Mental Workload ◽  
Time Estimation ◽  
Biosignal Analysis ◽  
Real Time Estimation

scholarly journals Anti-Heartbeat-Evoked Potentials Performance in Event-Related Potentials-Based Mental Workload Assessment

2021 ◽  
Vol 12 ◽  
Author(s):  
Sangin Park ◽  
Jihyeon Ha ◽  
Laehyun Kim
Keyword(s):  
Evoked Potentials ◽  
Mental Arithmetic ◽  
Mental Workload ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Event Related Potential ◽  
Support Vector ◽  
Arithmetic Task ◽  
Related Potentials ◽  
Mental Arithmetic Task

The aim of this study was to determine the effect of heartbeat-evoked potentials (HEPs) on the performance of an event-related potential (ERP)-based classification of mental workload (MWL). We produced low- and high-MWLs using a mental arithmetic task and measured the ERP response of 14 participants. ERP trials were divided into three conditions based on the effect of HEPs on ERPs: ERPHEP, containing the heartbeat in a period of 280–700ms in ERP epochs after the target; ERPA-HEP, not including the heartbeat within the same period; and ERPT, all trials including ERPA-HEP and ERPHEP. We then compared MWL classification performance using the amplitude and latency of the P600 ERP among the three conditions. The ERPA-HEP condition achieved an accuracy of 100% using a radial basis function-support vector machine (with 10-fold cross-validation), showing an increase of 14.3 and 28.6% in accuracy compared to ERPT (85.7%) and ERPHEP (71.4%), respectively. The results suggest that evoked potentials caused by heartbeat overlapped or interfered with the ERPs and weakened the ERP response to stimuli. This study reveals the effect of the evoked potentials induced by heartbeats on the performance of the MWL classification based on ERPs.

A machine for real-time estimation of the tribological characteristics of materials

2020 ◽  
Vol 86 (4) ◽  
pp. 61-65
Author(s):  
M. V. Abramchuk ◽  
R. V. Pechenko ◽  
K. A. Nuzhdin ◽  
V. M. Musalimov
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Tribological Characteristics ◽  
Continuous Processing ◽  
Friction Machine ◽  
Dynamic Friction Coefficient ◽  
Displacement Sensors ◽  
Frictional Interaction ◽  
Real Time Estimation ◽  
Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

Onboard Real-time Estimation of Mean Orbital Elements with Atmospheric Drag

2013 ◽  
Vol 39 (10) ◽  
pp. 1722
Author(s):  
Zhao-Wei SUN ◽  
Wei-Chao ZHONG ◽  
Shi-Jie ZHANG ◽  
Jian ZHANG
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Atmospheric Drag ◽  
Orbital Elements ◽  
Real Time Estimation

Soft sensor for real-time estimation of tablet potency in continuous direct compression manufacturing operation

2021 ◽  
Vol 602 ◽  
pp. 120624
Author(s):  
Reza Kamyar ◽  
David Lauri Pla ◽  
Anas Husain ◽  
Giuseppe Cogoni ◽  
Zilong Wang
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Soft Sensor ◽  
Direct Compression ◽  
Real Time Estimation

scholarly journals Real-Time Estimation of Microgrid Inertia and Damping Constant

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ujjwol Tamrakar ◽  
David A. Copp ◽  
Tu Nguyen ◽  
Timothy M. Hansen ◽  
Reinaldo Tonkoski
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Real Time Estimation

Estimation of Cognitive Distraction from Driver Gazing

2017 ◽  
Vol 9 (2) ◽  
pp. 50-60 ◽  
Author(s):  
Akira Yoshizama ◽  
Hiroyuki Nishiyama ◽  
Hirotoshi Iwasaki ◽  
Fumio Mizoguchi
Keyword(s):  
Qualitative Data ◽  
Mental Arithmetic ◽  
Support Vector ◽  
Simulation Environment ◽  
Time Intervals ◽  
Arithmetic Task ◽  
Task Load ◽  
Cognitive Distraction ◽  
Mental Arithmetic Task ◽  
Using Data

In their study, the authors sought to generate rules for cognitive distractions of car drivers using data from a driving simulation environment. They collected drivers' eye-movement and driving data from 18 research participants using a simulator. Each driver drove the same 15-minute course two times. The first drive was normal driving (no-load driving), and the second drive was driving with a mental arithmetic task (load driving), which the authors defined as cognitive-distraction driving. To generate rules of distraction driving using a machine-learning tool, they transformed the data at constant time intervals to generate qualitative data for learning. Finally, the authors generated rules using a Support Vector Machine (SVM).

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