EEG based cognitive task classification using multifractal detrended fluctuation analysis

Gaurav, .; Anand, R. S.; Kumar, Vinod

doi:10.1007/s11571-021-09684-z

Cited by 22 publications

(4 citation statements)

References 43 publications

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“…The above dashed regions probably mainly contain environmental sinusoidal noise, and the below one probably mainly contains sudden movement noise. In some studies, pre-processing methods such as band filtering and ICA are used to remove noise from raw EEG [8]- [12]. However, they rely heavily on human inspection, which is time-consuming and not generalized, and they cannot remove noise completely.…”

Section: Introductionmentioning

confidence: 99%

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG

Wang,

Zhao,

Jiang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some challenges in data quality and data size of EEG: (1) A large amount of noise is inevitable during EEG collection, making it difficult to extract discriminative features from raw EEG; (2) It is difficult to recruit a large number of subjects to collect sufficient and diverse data for model training. Both of the challenges cause the overfitting problem, especially for deep learning methods. In this paper, we propose DiffMDD, a diffusion-based deep learning framework for MDD diagnosis using EEG. Specifically, we extract more noise-irrelevant features to improve the model's robustness by designing the Forward Diffusion Noisy Training Module. Then we increase the size and diversity of data to help the model learn more generalized features by designing the Reverse Diffusion Data Augmentation Module. Finally, we re-train the classifier on the augmented dataset for MDD diagnosis. We conducted comprehensive experiments to test the overall performance and each module's effectiveness. The framework was validated on two public MDD diagnosis datasets, achieving the state-of-the-art performance.

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Section: Introductionmentioning

confidence: 99%

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG

Wang,

Zhao,

Jiang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Also, fractal methods have been used in this stage, especially for VMI data processing. Some include Hurst, Higuchi and Katz, and multifractal detrended fluctuation analysis [72][73][74][75][76]. In the classification stage of KMI analysis, there have been used algorithms such as logistic regression [68], linear discriminant analysis (LDA), SVM, and kNN [66].…”

Section: Introductionmentioning

confidence: 99%

Characterization and classification of kinesthetic motor imagery levels

Martinez-Peon,

Garcia-Hernandez,

Benavides-Bravo

et al. 2024

J. Neural Eng.

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Objective. Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach. A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0, 10, 40, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbours (kNN). Main results. A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN. Significance. Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

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“…Specifically, in recent years, multifractal analysis has received growing attention in biomedical engineering. For example, it was applied in the analysis of various biophysiological signals, including EEG [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ], ECG [ 11 , 12 , 13 , 14 , 15 ], magnetic resonance images and brainstem volume [ 16 , 17 , 18 ], mammograms [ 19 , 20 ], bone radiographic images [ 21 ], retina digital images [ 22 ], dental implant ultrasonic signal [ 23 ], and liver tissue images [ 24 ], to name few.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders

Lahmiri

Tadj

Gargour

2022

Entropy

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Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student t-test. The empirical results show that both expiration and inspiration signals exhibit clear evidence of multifractal properties under healthy and unhealthy conditions. In addition, expiration and inspiration signals exhibit more complexity under healthy conditions than under unhealthy conditions. Furthermore, distributions of multifractal characteristics are different across healthy and unhealthy conditions. Hence, this study improves the understanding of infant crying by providing a complete description of its intrinsic dynamics to better evaluate its health status.

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EEG based cognitive task classification using multifractal detrended fluctuation analysis

Cited by 22 publications

References 43 publications

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG

Characterization and classification of kinesthetic motor imagery levels

Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders

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