Evaluation Of Cognitive Workload From EEG During A Mental Arithmetic Task

2011 ◽  
Author(s):  
Brice Rebsamen ◽  
Kenneth Kwok ◽  
Trevor B. Penney
Keyword(s):  
Mental Arithmetic ◽  
Cognitive Workload ◽  
Arithmetic Task ◽  
Mental Arithmetic Task

scholarly journals Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1079
Author(s):  
Abhishek Varshney ◽  
Samit Kumar Ghosh ◽  
Sibasankar Padhy ◽  
Rajesh Kumar Tripathy ◽  
U. Rajendra Acharya
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Classification Accuracy ◽  
Mental Arithmetic ◽  
Cognitive Workload ◽  
Automated Classification ◽  
Eeg Signals ◽  
Arithmetic Task ◽  
Mental Arithmetic Task

The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN) models, such as long-short term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent unit (GRU), for the automated classification of mental-arithmetic-based cognitive workload tasks. Two cognitive workload classification strategies (bad mental arithmetic calculation (BMAC) vs. good mental arithmetic calculation (GMAC); and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC)) are considered in this work. The approach was evaluated using the publicly available mental arithmetic task-based EEG database. The results reveal that our proposed approach obtained classification accuracy values of 99.81%, 99.43%, and 99.81%, using the LSTM, BLSTM, and GRU-based RNN classifiers, respectively for the BMAC vs. GMAC cognitive workload classification strategy using all entropy features and a 10-fold cross-validation (CV) technique. The slope entropy features combined with each RNN-based model obtained higher classification accuracy compared with other entropy features for the classification of the BMAC vs. GMAC task. We obtained the average classification accuracy values of 99.39%, 99.44%, and 99.63% for the classification of the BFMAC vs. DMAC tasks, using the LSTM, BLSTM, and GRU classifiers with all entropy features and a hold-out CV scheme. Our developed automated mental arithmetic task system is ready to be tested with more databases for real-world applications.

scholarly journals Electroencephalograms during Mental Arithmetic Task Performance

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 14 ◽  
Author(s):  
Igor Zyma ◽  
Sergii Tukaev ◽  
Ivan Seleznov ◽  
Ken Kiyono ◽  
Anton Popov ◽  
...  
Keyword(s):  
Task Performance ◽  
Mental Arithmetic ◽  
Cognitive Workload ◽  
Arithmetic Task ◽  
Power Spectral ◽  
Neuroscience Research ◽  
Mental Arithmetic Task ◽  
Eeg Recordings ◽  
Detrended Fluctuation ◽  
Electroencephalogram Eeg

This work has been carried out to support the investigation of the electroencephalogram (EEG) Fourier power spectral, coherence, and detrended fluctuation characteristics during performance of mental tasks. To this aim, the presented dataset contains International 10/20 system EEG recordings from subjects under mental cognitive workload (performing mental serial subtraction) and the corresponding reference background EEGs. Based on the subtraction task performance (number of subtractions and accuracy of the result), the subjects were divided into good counters and bad counters (for whom the mental task required excessive efforts). The data was recorded from 36 healthy volunteers of matched age, all of whom are students of Educational and Scientific Centre “Institute of Biology and Medicine”, National Taras Shevchenko University of Kyiv (Ukraine); the recordings are available through Physiobank platform. The dataset can be used by the neuroscience research community studying brain dynamics during cognitive workload.

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).

scholarly journals Does visual exposure to one's pet regulate stress levels during the mental arithmetic task?

2021 ◽  
Author(s):  
Natalie Ein
Keyword(s):  
Stress Reactivity ◽  
Mental Arithmetic ◽  
Self Report ◽  
Induce Stress ◽  
Psychosocial Stressor ◽  
Arithmetic Task ◽  
Visual Exposure ◽  
Physiological Assessment ◽  
Mental Arithmetic Task

This thesis examined the role of viewing a picture of one’s pet as a mechanism for alleviating the symptoms of stress. The mental arithmetic task (MAT), a psychosocial stressor was used to induce stress. Participants were randomly assigned into one of six visual conditions: either a picture of their personal pet (n = 9), an unfamiliar animal (n = 9), a person who is supportive and important to the participant (n = 9), an unfamiliar person to the participant (n =8), a pleasant image (control 1) (n = 8) or no image (control 2) (n = 8). Stress reactivity, both physical (e.g., blood pressure) and subjective (self-reported anxiety), were measured. Findings indicated that contrary to the hypothesis, viewing a picture of one’s personal pet did not reduce stress reactivity, measured either subjectively (self-report) or objectively (physiological assessment). However, the study suggests that various images can influence stress reactivity.

scholarly journals Do Reward-Related Distractors Impair Cognitive Performance? Perhaps Not

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Dorottya Rusz ◽  
Erik Bijleveld ◽  
Michiel A. J. Kompier
Keyword(s):  
Cognitive Control ◽  
Cognitive Performance ◽  
Mental Arithmetic ◽  
Negative Impact ◽  
Arithmetic Task ◽  
Bayesian Analyses ◽  
Monetary Rewards ◽  
Mental Arithmetic Task ◽  
Task Irrelevant ◽  
Math Problems

Over a hundred prior studies show that reward-related distractors capture attention. It is less clear, however, whether and when reward-related distractors affect performance on tasks that require cognitive control. In this experiment, we examined whether reward-related distractors impair performance during a demanding arithmetic task. Participants (N = 81) solved math problems, while they were exposed to task-irrelevant stimuli that were previously associated with monetary rewards (vs. not). Although we found some evidence for reward learning in the training phase, results from the test phase showed no evidence that reward-related distractors harm cognitive performance. This null effect was invariant across different versions of our task. We examined the results further with Bayesian analyses, which showed positive evidence for the null. Altogether, the present study showed that reward-related distractors did not harm performance on a mental arithmetic task. When considered together with previous studies, the present study suggests that the negative impact of reward-related distractors on cognitive control is not as straightforward as it may seem, and that more research is needed to clarify the circumstances under which reward-related distractors harm cognitive control.

Electroencephalogram-based cognitive performance evaluation for mental arithmetic task

2021 ◽  
pp. 85-101
Author(s):  
Debatri Chatterjee ◽  
Rahul Gavas ◽  
Roopkatha Samanta ◽  
Sanjoy Kumar Saha
Keyword(s):  
Performance Evaluation ◽  
Cognitive Performance ◽  
Mental Arithmetic ◽  
Arithmetic Task ◽  
Mental Arithmetic Task
Sign in / Sign up

Export Citation Format

Share Document