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 Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Vladimir A. Maksimenko ◽  
Semen A. Kurkin ◽  
Elena N. Pitsik ◽  
Vyacheslav Yu. Musatov ◽  
Anastasia E. Runnova ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Motor Imagery ◽  
High Frequency ◽  
Classification Accuracy ◽  
Eeg Signals ◽  
Pass Filter ◽  
Time Frequency ◽  
Artificial Neural

We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8–13 Hz) and delta (1–5 Hz) brainwaves than in the high-frequency beta brainwave (15–30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

HALF-WAVE SEGMENT FEATURE EXTRACTION OF EEG SIGNALS OF PATIENTS WITH DEPRESSION AND PERFORMANCE EVALUATION OF NEURAL NETWORK CLASSIFIERS

2017 ◽  
Vol 17 (01) ◽  
pp. 1750006 ◽  
Author(s):  
SUBHA D. PUTHANKATTIL ◽  
PAUL K. JOSEPH
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Time Domain ◽  
Classification Accuracy ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Wavelet Entropy ◽  
Wavelet Energy ◽  
Significant Difference

A detailed understanding of key signal characteristics has enabled the use of artificial neural networks (ANN) for feature detection and classification of EEG signals in clinical research. The present study is performed to classify EEG signals of normal and depression patients with wavelet parameters as key input features. The characteristics of depression cannot be made out by visual inspection of EEG records unlike epilepsy which is well characterized by sudden recurrent and transient waveforms. In this study, a comparison is made between the performance of feedforward neural network (FFNN) and probabilistic neural network (PNN) while classifying the EEG signals of normal and depression patients. Classification capabilities of both the methods are validated with the EEG recordings from 30 normal controls and 30 depression patients. One-way ANOVA provided a statistical significant difference between the two classes of EEG signals recorded. Preprocessing for feature extraction is done using discrete wavelet transform (DWT). The time domain and relative wavelet energy (RWE) features calculated from the sub-bands are given as a set of input to the neural network. Another set of feature used independently for training the network is the wavelet entropy (WE). The FFNN achieved a classification accuracy of 100% and PNN gave an accuracy of 58.75% with time domain and wavelet energy as the input features. With wavelet entropy as the input feature, FFNN further showed 98.75% classification accuracy while PNN gave an accuracy of only 46.5%. The results indicate that FFNN with the given input features is more suitable for the classification of EEG signals with mood changing depressive disorders.

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.

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