Performance Evaluation of Empirical Mode Decomposition Algorithms for Mental Task Classification

Gupta, Akanksha; Kumar, Dhirendra; Chakraborti, Anirban; Sharma, Kiran

doi:10.1101/076646

Cited by 4 publications

(2 citation statements)

References 39 publications

(54 reference statements)

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“…As the added noise changes in each test, the resulting IMFs show no correlation with the respective IMFs from one test to another. Additional noise can be eliminated by averaging the IMFs collected from the various tests together [18].…”

Section: ) Empirical Mode Decomposition (Emd) and Ensemble Emdmentioning

confidence: 99%

Analysis of the Information Transfer Rate-ITR in Linear and Non-linear Feature Extraction Methods for SSVEP Signals

Cruz

Alfonso

Caicedo

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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The most popular paradigm in BCIs is the steady-state visually evoked potential (SSVEP) due to their advantages, such as the high information transfer rate (ITR), the time spent on users in the training phase, and the capacity to discriminate each stimulus. One of the most influential factors in the ITR evaluation is the feature extraction methods since these can increase the accuracy. Here, we compare nine methods for the extraction of features from SSVEP signals to identify those with better performance, according to the time window (TW), its technology (equipment and number of nodes), and the value of ITR. The study identifies two groups: the first one is characterized by presenting variations of correlated component analysis (CCA), which is highly used to increase the ITR due to its efficiency in classification and its capacity of response to reduction (TW), such as MsetCCA, IT-CCA, FBCCA; the second one are the representation special based methods that consider the non-linear nature of the electroencephalogram (EEG) signal such as TRCA, CORRCA, EMD, and VMD. The results show a considerable difference between these groups. The maximum ITR value for FBCCA was 117.75 [bits/min] in a TW of 1.25s, while the VMD method achieved 3120 [bits/min] in a TW of 1s, respectively. The comparison covers signals between 0.55 and 8 seconds, taking into account visual strain, the experimental environment, and other artifacts.

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Section: ) Empirical Mode Decomposition (Emd) and Ensemble Emdmentioning

confidence: 99%

Analysis of the Information Transfer Rate-ITR in Linear and Non-linear Feature Extraction Methods for SSVEP Signals

Cruz

Alfonso

Caicedo

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…HHT is an effective time–frequency analysis method for nonlinear and nonstationary signals [ 10 ]. Considering the nonlinear and nonstationary characteristics of EEG signals, some research has applied HHT to MI-BCIs, which has yielded promising results [ 11 – 14 ]. Some research also used the improved algorithm for empirical mode decomposition to classify motor imagery or other types of EEG signals such as epilepsy or depth of anesthesia [ 15 – 18 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Visual Imagery by Electroencephalography Based on Empirical Mode Decomposition and an Autoregressive Model

Gong

et al. 2022

Computational Intelligence and Neuroscience

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The traditional imagery task for brain–computer interfaces (BCIs) consists of motor imagery (MI) in which subjects are instructed to imagine moving certain parts of their body. This kind of imagery task is difficult for subjects. In this study, we used a less studied yet more easily performed type of mental imagery—visual imagery (VI)—in which subjects are instructed to visualize a picture in their brain to implement a BCI. In this study, 18 subjects were recruited and instructed to observe one of two visual-cued pictures (one was static, while the other was moving) and then imagine the cued picture in each trial. Simultaneously, electroencephalography (EEG) signals were collected. Hilbert–Huang Transform (HHT), autoregressive (AR) models, and a combination of empirical mode decomposition (EMD) and AR were used to extract features, respectively. A support vector machine (SVM) was used to classify the two kinds of VI tasks. The average, highest, and lowest classification accuracies of HHT were 68.14 ± 3.06%, 78.33%, and 53.3%, respectively. The values of the AR model were 56.29 ± 2.73%, 71.67%, and 30%, respectively. The values obtained by the combination of the EMD and the AR model were 78.40 ± 2.07%, 87%, and 48.33%, respectively. The results indicate that multiple VI tasks were separable based on EEG and that the combination of EMD and an AR model used in VI feature extraction was better than an HHT or AR model alone. Our work may provide ideas for the construction of a new online VI-BCI.

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Recognition of multi-cognitive tasks from EEG signals using EMD methods

Gupta

Kumar

Verma

et al. 2022

Neural Comput & Applic

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Mental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC; two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification.

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Performance Evaluation of Empirical Mode Decomposition Algorithms for Mental Task Classification

Cited by 4 publications

References 39 publications

Analysis of the Information Transfer Rate-ITR in Linear and Non-linear Feature Extraction Methods for SSVEP Signals

Analysis of the Information Transfer Rate-ITR in Linear and Non-linear Feature Extraction Methods for SSVEP Signals

Identification of Visual Imagery by Electroencephalography Based on Empirical Mode Decomposition and an Autoregressive Model

Recognition of multi-cognitive tasks from EEG signals using EMD methods

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