Real-Time Mental Arithmetic Task Recognition From EEG Signals

2013 ◽  
Vol 21 (2) ◽  
pp. 225-232 ◽  
Author(s):  
Qiang Wang ◽  
Olga Sourina
Keyword(s):  
Real Time ◽  
Mental Arithmetic ◽  
Eeg Signals ◽  
Arithmetic Task ◽  
Mental Arithmetic Task ◽  
Task Recognition

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 SMORASO-DT : A hybrid machine learning classification model to classify individuals based on working memory load in mental arithmetic task

2020 ◽  
Author(s):  
K R Shivabalan ◽  
Brototo Deb ◽  
Shivam Goel ◽  
R Arivan
Keyword(s):  
Nonlinear Dynamics ◽  
Machine Learning ◽  
Memory Load ◽  
Mental Arithmetic ◽  
Classification Model ◽  
Eeg Signals ◽  
Arithmetic Task ◽  
Chaotic Dynamical System ◽  
Mental Arithmetic Task ◽  
Hybrid Machine

AbstractNonlinear dynamics and chaos theory are being widely used nowadays in neuroscience to characterize complex systems within which the change of the output is not proportional to the change applied at the input. Such nonlinear systems compared to linear systems, often appear chaotic, unpredictable, or counterintuitive, however, yet their behaviour is not mapped out as random. Thus, hidden potential of the dynamical properties of the physiological phenomenon can be detected by these approaches especially to elucidate the complex human brain activity gathered from the electroencephalographic (EEG) signals. As it is known, brain is a chaotic dynamical system and its generated EEG signals are generally chaotic because, with respect to time, the amplitude changes continuously. A reliable and non-invasive measurement of memory load, to measure continuously while performing a cognitive task, is highly desirable to assess cognitive functions, crucial for prevention of decision-making errors. Such measurements help to keep up the efficiency and productivity in task completion, work performance, and to avoid cognitive overload, especially at high mental or physical workload places like traffic control, military operations, and rescue commands. In this work, we have measured the linear and nonlinear dynamics of the EEG signals in subjects undergoing mental arithmetic task. Further, we have also differentiated the subjects who can perform a mental task good or bad, and developed a hybrid machine learning model, the SMORASO-DT (SMOte + Random forest + lASso-Decision Tree), to differentiate good and bad performers during n-back task state with an accuracy rate of 78%.

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.

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.

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