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Proceedings ArticleDOI

Variational Mode Decomposition Based Mental Task Classification from Electroencephalogram

TL;DR: In this paper, the authors proposed a mental task classification method using variational mode decomposition (VMD)-based novel feature extraction from single-channel EEG, with three stages: signal decomposition using VMD; computation of proposed variational modes energy ratio; classification using adaptive boosting algorithm.
Abstract: Selective feature extraction from single-channel electroencephalogram signal provides appropriate classification of mental tasks, which is crucial for designing mobile brain-computer interface and neuro-bio-feedback systems. However, existing features deteriorate in the presence of artifacts. Therefore, we propose a mental task classification method using variational mode decomposition (VMD)-based novel feature extraction from single-channel EEG, with three stages: Signal decomposition using VMD; computation of proposed variational mode energy ratio; classification using adaptive boosting algorithm. The proposed method is evaluated using artifact-free and contaminated EEG signals from EEG during mental arithmetic task (EEGMAT) database and self-acquired (SA) database recorded using single-channel device. Average subject-specific accuracies of 93% and 96% for classification of baseline and serial-subtraction task have been achieved in EEGMAT and SA databases respectively. Extensive comparative analysis exhibits the superiority of proposed feature as compared to existing features in terms of accurate classification of baseline-mental task, and robustness under artifactual EEG signals.
Citations
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Journal ArticleDOI
TL;DR: 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).
Abstract: 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 multi-channel 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.

10 citations

Journal ArticleDOI
TL;DR: In this paper , a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data, which is non-invasive and aims to extract mental task specific features from EEG data acquired from a particular subject.
Abstract: BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Deep learning frameworks are popular among researchers for analyzing both spatial and time series data, making them well-suited for classifying EEG signals. METHOD. In this paper, a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data. Pre-computed features of EEG signals were obtained after raw EEG signals acquired from the subjects were spatially filtered by applying the Laplacian surface. To handle high-dimensional data, principal component analysis (PCA) was performed which helps in the extraction of most discriminating features from input vectors. RESULT. The proposed model is non-invasive and aims to extract mental task-specific features from EEG data acquired from a particular subject. The training was performed on the average combined Power Spectrum Density (PSD) values of all but one subject. The performance of the proposed model based on a deep neural network (DNN) was evaluated using a benchmark dataset. We achieved 77.62% accuracy. CONCLUSION. The performance and comparison analysis with the related existing works validated that the proposed cross-subject classification framework outperforms the state-of-the-art algorithm in terms of performing an accurate mental task from EEG signals.

3 citations

References
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Journal ArticleDOI
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...

11,407 citations

Journal ArticleDOI
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Abstract: During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal, while each being smooth after demodulation into baseband. In Fourier domain, this corresponds to a narrow-band prior. We show important relations to Wiener filter denoising. Indeed, the proposed method is a generalization of the classic Wiener filter into multiple, adaptive bands. Our model provides a solution to the decomposition problem that is theoretically well founded and still easy to understand. The variational model is efficiently optimized using an alternating direction method of multipliers approach. Preliminary results show attractive performance with respect to existing mode decomposition models. In particular, our proposed model is much more robust to sampling and noise. Finally, we show promising practical decomposition results on a series of artificial and real data.

4,111 citations

Journal ArticleDOI
TL;DR: The feasibility of establishing an alternative mode of communication between man and his surroundings using only the subject's brain waves was studied, indicating that it is possible to accurately distinguish between any two of the five tasks investigated.
Abstract: The feasibility of establishing an alternative mode of communication between man and his surroundings was studied. The form of communication proposed uses only the subject's brain waves, with no overt physical action required. The subject's electroencephalograms (EEG) were recorded while various mental tasks designed to elicit hemispheric responses were performed. Features formed from the EEG recording were then used as inputs into a Bayes quadratic classifier to test classification accuracy between the various tasks. The results obtained indicate that it is possible to accurately distinguish between any two of the five tasks investigated. A comparison between three different methods for creating the feature sets is also presented. >

466 citations

Journal ArticleDOI
TL;DR: A narrow band analysis is made to detect those EEG frequencies that change selectively during the performance of a mental task that requires attention to internal processing.

401 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a signal quality-aware Internet of Things (IoT)-enabled electrocardiogram (ECG) telemetry system for continuous cardiac health monitoring applications.
Abstract: In this paper, we propose a novel signal quality-aware Internet of Things (IoT)-enabled electrocardiogram (ECG) telemetry system for continuous cardiac health monitoring applications. The proposed quality-aware ECG monitoring system consists of three modules: 1) ECG signal sensing module; 2) automated signal quality assessment (SQA) module; and 3) signal-quality aware (SQAw) ECG analysis and transmission module. The main objectives of this paper are: design and development of a light-weight ECG SQA method for automatically classifying the acquired ECG signal into acceptable or unacceptable class and real-time implementation of proposed IoT-enabled ECG monitoring framework using ECG sensors, Arduino, Android phone, Bluetooth, and cloud server. The proposed framework is tested and validated using the ECG signals taken from the MIT-BIH arrhythmia and Physionet challenge databases and the real-time recorded ECG signals under different physical activities. Experimental results show that the proposed SQA method achieves promising results in identifying the unacceptable quality of ECG signals and outperforms existing methods based on the morphological and RR interval features and machine learning approaches. This paper further shows that the transmission of acceptable quality of ECG signals can significantly improve the battery lifetime of IoT-enabled devices. The proposed quality-aware IoT paradigm has great potential for assessing clinical acceptability of ECG signals in improvement of accuracy and reliability of unsupervised diagnosis system.

264 citations

Trending Questions (1)
Variational Mode Decomposition in markeing?

The provided paper is about using Variational Mode Decomposition (VMD) for mental task classification from electroencephalogram (EEG) signals. There is no mention of using VMD in marketing in the paper.