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.

EEG coherence in a mental arithmetic task performance in first episode schizophrenia and schizoaffective disorder

2018 ◽  
Vol 129 (11) ◽  
pp. 2315-2324 ◽  
Author(s):  
Yuliya Zaytseva ◽  
Zhanna Garakh ◽  
Vladimir Novototsky-Vlasov ◽  
Isaac Ya. Gurovich ◽  
Alexander Shmukler ◽  
...  
Keyword(s):  
Task Performance ◽  
Schizoaffective Disorder ◽  
Mental Arithmetic ◽  
First Episode ◽  
Eeg Coherence ◽  
Arithmetic Task ◽  
Mental Arithmetic Task ◽  
First Episode Schizophrenia

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 Task-induced deactivation in diverse brain systems correlates with interindividual differences in distinct autonomic indices

2018 ◽  
Author(s):  
Vittorio Iacovella ◽  
Luca Faes ◽  
Uri Hasson
Keyword(s):  
Heart Rate ◽  
Individual Differences ◽  
Task Performance ◽  
Mental Arithmetic ◽  
Block Design ◽  
Large Set ◽  
Arithmetic Task ◽  
Activation Patterns ◽  
Fmri Study ◽  
Mental Arithmetic Task

AbstractNeuroimaging research has shown that different cognitive tasks induce relatively specific activation patterns, as well as less task-specific deactivation patterns. Here we examined whether individual differences in Autonomic Nervous System (ANS) activity during task performance correlate with the magnitude of task-induced deactivation. In an fMRI study, participants performed a continuous mental arithmetic task in a task/rest block design, while undergoing combined fMRI and heart / respiration rate acquisitions using photoplethysmograph and respiration belt. As expected, task performance increased heart-rate and reduced the RMSSD, a cardiac index related to vagal tone. Across participants, higher heart rate during task was linked to increased activation in fronto-parietal regions, as well as to stronger deactivation in ventromedial prefrontal regions. Respiration frequency during task was associated with similar patterns, but in different regions than those identified for heart-rate. Finally, in a large set of regions, almost exclusively limited to the Default Mode Network, lower RMSSD was associated with greater deactivation, and furthermore, the vast majority of these regions were task-deactivated at the group level. Together, our findings show that inter-individual differences in ANS activity are strongly linked to task-induced deactivation. Importantly, our findings suggest that deactivation is a multifaceted construct potentially linked to ANS control, because distinct ANS measures correlate with deactivation in different regions. We discuss the implications for current theories of cortical control of the ANS and for accounts of deactivation, with particular reference to studies documenting a “failure to deactivate” in multiple clinical states.

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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