EEG Based Mental Arithmetic Task Classification Using a Stacked Long Short Term Memory Network for Brain-Computer Interfacing

Ganguly, Biswarup; Chatterjee, Arpan; Mehdi, Waqar; Sharma, Soumyadip; Garai, Soumyadeep

doi:10.1109/vlsidcs47293.2020.9179949

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…Sharma L. D. et al (2021) utilized 1D representation of entropy-based features and SVM to classify mental arithmetic tasks on a publicly available "EEG Mental Arithmetic Dataset" by Physionet PhysioBank (2000), Zyma et al (2019) and achieved an accuracy of 94%. To classify mental arithmetic vs. rest, the stacked long-short term memory (LSTM) was applied to raw 1D EEG signals, resulting in an accuracy of 93.59% in the study by Ganguly et al (2020). Behrouzi and Hatzinakos, (2022) elaborated the benefits of deep learningbased graph variational auto-encoder on 2D representation to detect the task of mental arithmetic attaining a peak mean performance of 95%.…”

Section: Introductionmentioning

confidence: 99%

An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods

Shah

Sinha

2022

Front. Signal Process.

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Electroencephalographic (EEG) signals are electrical signals generated in the brain due to cognitive activities. They are non-invasive and are widely used to assess neurodegenerative conditions, mental load, and sleep patterns. In this work, we explore the utility of representing the inherently single dimensional time-series in different dimensions such as 1D-feature vector, 2D-feature maps, and 3D-videos. The proposed methodology is applied to four diverse datasets: 1) EEG baseline, 2) mental arithmetic, 3) Parkinson’s disease, and 4) emotion dataset. For a 1D analysis, popular 1D features hand-crafted from the time-series are utilized for classification. This performance is compared against the data-driven approach of using raw time-series as the input to the deep learning framework. To assess the efficacy of 2D representation, 2D feature maps that utilize a combination of the Feature Pyramid Network (FPN) and Atrous Spatial Pyramid Pooling (ASPP) is proposed. This is compared against an approach utilizing a composite feature set consisting of 2D feature maps and 1D features. However, these approaches do not exploit spatial, spectral, and temporal characteristics simultaneously. To address this, 3D EEG videos are created by stacking spectral feature maps obtained from each sub-band per time frame in a temporal domain. The EEG videos are the input to a combination of the Convolution Neural Network (CNN) and Long–Short Term Memory (LSTM) for classification. Performances obtained using the proposed methodologies have surpassed the state-of-the-art for three of the classification scenarios considered in this work, namely, EEG baselines, mental arithmetic, and Parkinson’s disease. The video analysis resulted in 92.5% and 98.81% peak mean accuracies for the EEG baseline and EEG mental arithmetic, respectively. On the other hand, for distinguishing Parkinson’s disease from controls, a peak mean accuracy of 88.51% is achieved using traditional methods on 1D feature vectors. This illustrates that 3D and 2D feature representations are effective for those EEG data where topographical changes in brain activation regions are observed. However, in scenarios where topographical changes are not consistent across subjects of the same class, these methodologies fail. On the other hand, the 1D analysis proves to be significantly effective in the case involving changes in the overall activation of the brain due to varying degrees of deterioration.

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Section: Introductionmentioning

confidence: 99%

An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods

Shah

Sinha

2022

Front. Signal Process.

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“…Although these methods can provide satisfactory performance, they do not provide any solution for selecting a suitable number of signal decomposition levels [13]. Recently, deep learning methods like Convolution Neural Network, long short-term memory (LSTM) were proposed by authors [14], [15] combines feature extraction and classification in one architecture requiring large sized GPU.…”

Section: Introductionmentioning

confidence: 99%

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

Mathur

Chakka

2022

IEEE Sensors J.

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Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multichannel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs.

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“…They extracted the variance, energy, and entropy features from the Fourier domain intrinsic band functions of EEG data and used an SVM classifier to classify the mental arithmetic tasks, such as BFMAC and DMAC, respectively, and reported an accuracy value of 98.60%. The stacked long short-term memory (LSTM)-based model was used to classify mental arithmetic tasks using spectral and instantaneous frequency features obtained from the multi-channel EEG signals [15].…”

Section: Introductionmentioning

confidence: 99%

“…The methods reported in [13][14][15] considered the classification schemes, such as BF-MAC vs. DMAC, using only the statistical and spectral features from EEG signals. Similarly, the method reported by [9] included a mental arithmetic task recognition framework using EEG signal features.…”

Section: Introductionmentioning

confidence: 99%

“…It considers the information from the previous state and input to evaluate the output at the present time-step. The LSTM-based RNN model has been used for mental arithmetic task classification using EEG signal features [15]. The RNN models can exploit the correlations between the features of EEG signals in different time-steps for the classification of mental arithmetic tasks.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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EEG Based Mental Arithmetic Task Classification Using a Stacked Long Short Term Memory Network for Brain-Computer Interfacing

Cited by 17 publications

References 19 publications

An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods

An investigation of the multi-dimensional (1D vs. 2D vs. 3D) analyses of EEG signals using traditional methods and deep learning-based methods

Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals

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

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