scholarly journals Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Selina C. Wriessnegger ◽  
Philipp Raggam ◽  
Kyriaki Kostoglou ◽  
Gernot R. Müller-Putz
Keyword(s):  
Riemannian Geometry ◽  
Response Times ◽  
Mental Workload ◽  
Steady Increase ◽  
Behavioral Measures ◽  
Alpha Frequency ◽  
Brain Signals ◽  
Task Conditions ◽  
Electroencephalogram Eeg ◽  
Experiment Duration

The goal of this study was to implement a Riemannian geometry (RG)-based algorithm to detect high mental workload (MWL) and mental fatigue (MF) using task-induced electroencephalogram (EEG) signals. In order to elicit high MWL and MF, the participants performed a cognitively demanding task in the form of the letter n-back task. We analyzed the time-varying characteristics of the EEG band power (BP) features in the theta and alpha frequency band at different task conditions and cortical areas by employing a RG-based framework. MWL and MF were considered as too high, when the Riemannian distances of the task-run EEG reached or surpassed the threshold of the baseline EEG. The results of this study showed a BP increase in the theta and alpha frequency bands with increasing experiment duration, indicating elevated MWL and MF that impedes/hinders the task performance of the participants. High MWL and MF was detected in 8 out of 20 participants. The Riemannian distances also showed a steady increase toward the threshold with increasing experiment duration, with the most detections occurring toward the end of the experiment. To support our findings, subjective ratings (questionnaires concerning fatigue and workload levels) and behavioral measures (performance accuracies and response times) were also considered.

scholarly journals Mental Workload Classification Method Based on EEG Independent Component Features

2020 ◽  
Vol 10 (9) ◽  
pp. 3036 ◽  
Author(s):  
Hongquan Qu ◽  
Yiping Shan ◽  
Yuzhe Liu ◽  
Liping Pang ◽  
Zhanli Fan ◽  
...  
Keyword(s):  
Human Resources ◽  
Classification Accuracy ◽  
Mental Workload ◽  
Independent Component ◽  
Classification Method ◽  
Speech Signals ◽  
Eeg Signals ◽  
Independent Components ◽  
Brain Signals ◽  
Electroencephalogram Eeg

Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.

scholarly journals Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Gerolf Vanacker ◽  
José del R. Millán ◽  
Eileen Lew ◽  
Pierre W. Ferrez ◽  
Ferran Galán Moles ◽  
...  
Keyword(s):  
Control System ◽  
Shared Control ◽  
Eeg Signals ◽  
Intelligent Processing ◽  
Intelligent Wheelchair ◽  
Brain Signals ◽  
Brain Interface ◽  
The Subject ◽  
Electroencephalogram Eeg ◽  
The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

scholarly journals Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle

2020 ◽  
Vol 10 (10) ◽  
pp. 3532
Author(s):  
Jesús Jaime Moreno Escobar ◽  
Oswaldo Morales Matamoros ◽  
Ricardo Tejeida Padilla ◽  
Ixchel Lina Reyes ◽  
Liliana Chanona Hernández ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Doctors ◽  
Eeg Signals ◽  
Smart System ◽  
Brain Signals ◽  
Efficiency Assessment ◽  
Power Spectral ◽  
Electroencephalogram Eeg ◽  
Perception Action

This work presents the HSS-Cognitive project, which is a Healthcare Smart System that can be applied in measuring the efficiency of any therapy where neuronal interaction gives a trace whether the therapy is efficient or not, using mathematical tools. The artificial intelligence of the project underlies in the understanding of brain signals or Electroencephalogram (EEG) by means of the determination of the Power Spectral Density (PSD) over all the EEG bands in order to estimate how efficient was a therapy. Our project HSS-Cognitive was applied, recording the EEG signals from two patients treated for 8 min in a dolphin tank, measuring their activity in five experiments and for 6 min measuring their activity in a pool without dolphin in four experiments. After applying our TEA (Therapeutic Efficiency Assessment) metric for patient 1, we found that this patient had gone from having relaxation states regardless of the dolphin to attention states when the dolphin was presented. For patient 2, we found that he had maintained attention states regardless of the dolphin, that is, the DAT (Dolphin Assisted Therapy) did not have a significant effect in this patient, perhaps because he had a surgery last year in order to remove a tumor, having impact on the DAT effectiveness. However, patient 2 presented the best efficiency when doing physical therapy led by a therapist in a pool without dolphins around him. According to our findings, we concluded that our Brain-Inspired Healthcare Smart System can be considered a reliable tool for measuring the efficiency of a dolphin-assisted therapy and not only for therapist or medical doctors but also for researchers in neurosciences.

EEG Based BCI Using Visual Imagery Task for Robot Control

2013 ◽  
Vol 61 (2) ◽  
Author(s):  
Husnaini Azmy ◽  
Norlaili Mat Safri
Keyword(s):  
Spectral Power ◽  
Normal Subjects ◽  
Imagery Task ◽  
Non Invasive ◽  
Cortex Area ◽  
Wireless Robot ◽  
Scalp Location ◽  
Task Conditions ◽  
Electroencephalogram Eeg ◽  
The Brain

The aim of this study is to detect the brain activation on scalp by Electroencephalogram (EEG) task–based for brain computer interface (BCI) using wirelessly control robot. EEG was measured in 8 normal subjects for control and task conditions. The objective is to determine one scalp location which will give signals that can be used to control the wireless robot using BCI and EEG, using non invasive and without subject training. In control condition subjects were ask to relax but in task condition, subjects were asked to imagine a star rotating clockwise at position 45 degrees direction pointed by the wireless robot where at this angle the target is located. At position 0 and 90 degree angle subjects were asked to relax since there is no target on that direction. Using EEG spectral power analysis and normalization, the optimum location for this task has been detected at position F8 which is in frontal cortex area and the rhythm happened at alpha frequency band. At this position, the signals from the brain should be able to drive the robot to the required direction by giving correct and accurate signals to robot moving towards target.

BCI-Touch Based System: A Multimodal CAD Interface for Object Manipulation

2013 ◽  
Author(s):  
Rohit Bhat ◽  
Akshay Deshpande ◽  
Rahul Rai ◽  
Ehsan Tarkesh Esfahani
Keyword(s):  
Computer Aided Design ◽  
Brain Activity ◽  
Temporal Cortex ◽  
Target Object ◽  
Time Frequency ◽  
System A ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Aided Design ◽  
Electroencephalogram Eeg

The aim of this paper is to explore a new multimodal Computer Aided Design (CAD) platform based on brain-computer interfaces and touch based systems. The paper describes experiments and algorithms for manipulating geometrical objects in CAD systems using touch-based gestures and movement imagery detected though brain waves. Gestures associated with touch based systems are subjected to ambiguity since they are two dimensional in nature. Brain signals are considered here as the main source to resolve these ambiguities. The brainwaves are recorded in terms of electroencephalogram (EEG) signals. Users wear a neuroheadset and try to move and rotate a target object on a touch screen. As they perform these actions, the EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed in the time-frequency domain to detect the desynchronizations of certain frequency bands (3–7Hz, 8–13 Hz, 14–20Hz 21–29Hz and 30–50Hz) in the temporal cortex as an indication of motor imagery.

3104 Differences of Brake Response Times due to Drivers' Mental Workload for Information Processing

2007 ◽  
Vol 2007.16 (0) ◽  
pp. 261-262
Author(s):  
Kazumoto MORITA ◽  
Masaya OKAMOTO ◽  
Yoshinobu UCHIYAMA ◽  
Michiaki SEKINE
Keyword(s):  
Information Processing ◽  
Response Times ◽  
Mental Workload

scholarly journals Work Performance Measurement of Data Entry Employees in E-Commerce Industry Based on Mental Workload Value

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Iwan Aang Soenandi
Keyword(s):  
Work Performance ◽  
Mental Workload ◽  
Data Entry ◽  
Time Measurement ◽  
Task Load ◽  
Factors Influencing ◽  
Before And After ◽  
Reaction Time Measurement ◽  
Nasa Tlx ◽  
Electroencephalogram Eeg

This reseach aimed to measure the mental workload of data entry processing tasks in the e-commerce industry based on mental workload value. It was to determine the factors influencing mental workload mainly induced by the data entry process. The experiments without work instruction and with two types of work instruction were conducted to diagnose the mental workload. The measurement of the initial mental workload condition of data entry employees was conducted in the laboratory. Then, the Electroencephalogram (EEG) measurement using sensors from Emotiv was performed every 30 minutes, and the data of EEG measurements (focus, engagement, and stress) were collected using the laptop. Meanwhile, pulse measurement (heart rate) was measured before and after the work. Raw National Aeronautics and Space Administration Task Load Index (NASA-TLX) and reaction time measurement were conducted after the work. Through these experiments, the researchers identify that mental effort and fatigue are the significant determinants of mental workload value in the data entry process of the e-commerce industry. In respect of the results of work performance analysis, it is recommended that the placement of work instruction should be near the employee. Then, the task demand (minimum completion target) should be adjusted according to each employee’s capacity.

scholarly journals Using Psychophysiological Sensors to Assess Mental Workload in Web Browsing

2017 ◽  
Author(s):  
Angel Jimenez-Molina ◽  
Cristian Retamal ◽  
Hernan Lira
Keyword(s):  
High Frequency ◽  
Mental Workload ◽  
Electrodermal Activity ◽  
Eye Gaze ◽  
Physiological Responses ◽  
Web Browsing ◽  
Web Page ◽  
Non Invasive ◽  
Four Levels ◽  
Electroencephalogram Eeg

The mental workload induced by a Web page is essential for improving the user’s browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. In order to face this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify real-time mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmography (PPG), electroencephalogram (EEG), temperature and eye gaze) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves on average four levels of mental workload. Also, by combining EEG with the PPG and EDA, the accuracy of the classification reaches 95.73 %.

Hybrid BCI Controller for a Semi-Autonomous Wheelchair

2019 ◽  
Author(s):  
Vidya K. Nandikolla ◽  
Travis K. van Leeuwen ◽  
Amiel Hartman
Keyword(s):  
Evoked Potential ◽  
Moving Objects ◽  
Data Transfer ◽  
Response Times ◽  
Extended Period ◽  
Data Transfer Rate ◽  
Transfer Rates ◽  
Smart Wheelchairs ◽  
Lower Accuracy ◽  
Electroencephalogram Eeg

Abstract Smart wheelchairs with semi or fully autonomous functions, can greatly improve the mobility of physically impaired persons. However, most are controlled using inputs that require physical manipulation (e.g. joystick controllers) and for persons with severe physical impairments this method of control can be too demanding. A noninvasive brain-computer interface (BCI) technology-based controller could bridge between the smart wheelchairs users and physically impaired persons with severe conditions. Current BCI controlled wheelchairs rely on detecting steady-state visually evoked potential (SSVEP) responses as these typically have the greatest data transfer rate. However, this method requires the user to focus on a screen for an extended period of time. This causes strain on the user and takes their attention away from their surroundings, which could be dangerous in a scenario that requires navigation around multiple moving objects. The focus of this project is to design a hybrid BCI controller using an electroencephalogram (EEG) headset to detect hand motor imagery (MI) and jaw electromyography (EMG) signals to control a smart wheelchair in conjunction with its semi-autonomous capabilities. A controller of this kind is well-known to have low data transfer rates, and therefore has lower accuracy and longer response times as compared to other controllers. However, a properly structured controller hierarchy between the BCI controller and semi-autonomous system is developed to compensate the limitations of the controller’s accuracy.

How reading in single- and multiple-column types influence our cognitive load: an EEG study

2019 ◽  
Vol 37 (4) ◽  
pp. 593-606 ◽  
Author(s):  
Hosam Al-Samarraie ◽  
Atef Eldenfria ◽  
Fahed Zaqout ◽  
Melissa Lee Price
Keyword(s):  
Cognitive Load ◽  
Cognitive Processing ◽  
Content Type ◽  
Brain Signals ◽  
Significant Difference ◽  
Reading Text ◽  
The Impact ◽  
Electroencephalogram Eeg ◽  
Instructional Design Practices ◽  
The Brain

Purpose The impact of different screen-based typography styles on individuals’ cognitive processing of information has not been given much consideration in the literature, though such differences would imply different learning outcomes. This study aims to enrich the current understanding of the impact of reading in single- and multiple-column types on students’ cognitive processing. Design/methodology/approach An electroencephalogram (EEG) was used to record and analyze the brain signals of 27 students while reading from single- and multiple- column layouts. Findings The results showed a significant difference in students’ cognitive load when reading text from different types of columns. All students exerted less processing efforts when text was presented in two-column format, thus experiencing less cognitive load. Originality/value Using EEG, this study examined the neural consequences of reading in single- and multiple-column types on cognitive load during reading. The findings can be used to enrich the current instructional design practices on how different typographical formats facilitate learners’ cognitive performance.

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