An efficient method for classifying motor imagery using CPSO-trained ANFIS prediction

Mosavi, Mohammad Reza; Ayatollahi, Ahmad; Afrakhteh, Sajjad

doi:10.1007/s12530-019-09280-x

Cited by 22 publications

(7 citation statements)

References 42 publications

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“…28 Mosavi et al have proposed an adaptive neuro-fuzzy inference system (ANFIS) trained with a recently developed corrected particle swarm optimization (CPSO) algorithm to classify common spatial pattern (CSP)-based MI-EEG features. 29 This study achieved an average accuracy rate of 96.64% by applying the proposed method on dataset 1 from BCI competition IV. In a study carried out by Zhang et al, 30 a novel entropy-based feature weighting strategy was proposed, which can automatically reduce irrelevant features.…”

Section: Introductionmentioning

confidence: 83%

“…A comparison of the results of the experiments indicates that the ELM classifier, with an average accuracy of 80.7%, provides a superior classification to the SVM, with an accuracy of 72.8% 28 . Mosavi et al have proposed an adaptive neuro‐fuzzy inference system (ANFIS) trained with a recently developed corrected particle swarm optimization (CPSO) algorithm to classify common spatial pattern (CSP)‐based MI‐EEG features 29 . This study achieved an average accuracy rate of 96.64% by applying the proposed method on dataset 1 from BCI competition IV.…”

Section: Introductionmentioning

confidence: 99%

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Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

Alizadeh

Afrakhteh

Mosavi

2023

Int J Imaging Syst Tech

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Brain–computer interfaces (BCI) have begun to revolutionize many aspects of human life, ranging from health to smart living and communication devices. Therefore, BCI technologies require accurate recognition and classification systems of the brain responses to a variety of motor imagery (MI) movements through electroencephalogram (EEG) signals. Nowadays, deep learning models have received a lot of interest for learning features and classifying numerous kinds of data. However, it has not been thoroughly investigated for EEG signal classification. In this study, to reach an improved performance in MI‐EEG signal classification, we proposed a new strategy based on continuous wavelet transform (CWT)‐based time‐frequency maps to generate two‐dimensional (2D) EEG images of adaptively reconstructed signals from the extracted multivariate mode decomposition (MVMD) modes. In the next step, the extracted signals are projected in the new space using the proposed multiclass common spatial pattern (MCCSP) filtering. The resulting images are then fed to the convolutional neural network (CNN) architectures (AlexNet and LeNet). The proposed framework has the benefit of achieving high classification accuracy to be attained even with a significant amount of input data. LeNet and AlexNet reach the best average accuracy rates of 95.33% and 93.66% on dataset 1 from BCI competition IV and the results on dataset 2a from competition IV are more promising than the current state of the arts. Our results depict that MI‐EEG task recognition using image classification approaches, along with CNNs, is as comparable or even superior to other existing traditional approaches and provides high potential for upcoming research.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

Alizadeh

Afrakhteh

Mosavi

2023

Int J Imaging Syst Tech

Self Cite

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“…While applying ANFIS, rules are created and a model is obtained as much as the number of rules created. In contrast to the ANOVA model on which the experiment planning is based, the RMSE of ANFIS is obtained with a lower value since the number of models tested in ANFIS is much higher (Mosavi et. al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Using ANOVA and ANFİS Approaches in Modelling Agricultural Experiments

GÖKKUŞ

Şentürk

Alatürk

et al. 2022

Türk Tarım Ve Doğa Bilimleri Dergisi

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Adaptive Neuro-Fuzzy Inference System (ANFIS) can analyze the factors and factor levels affecting the subject of interest in many branches such as technology, production, health, social and education, depending on the many rules it creates and with a very small experimental error (RMSE). and modelling. It is also applied in the field of agriculture, especially for the solution of problems such as agricultural field selection or technological product development. On the other hand, classical statistical methods are generally used in due diligence studies in a certain time period, such as product cultivation. Experimental design methods or in other words analysis of variance (ANOVA) methods come first among these methods. With the experiments modeled by ANOVA, the factors affecting the subject of interest and the levels of these factors are analyzed according to a single rule of the method used. Since the Root Mean Square Error (RMSE) of the model formed by the multiple rules of ANFIS versus the single rule of ANOVA is much smaller, it gives stronger results. Modeling agricultural products with ANFIS depending on time will support data mining studies in this field. In this study, first both ANOVA and ANFIS methods were briefly explained, and then the data of a due diligence study carried out in agriculture were modeled by both methods and similar findings were obtained. However, mostly the standard deviation (RMSE) values of ANFIS were found to be smaller than ANOVA. In addition, the relationships between ANFIS outputs and real measurements were examined.

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“…Thus the adaptive neuro-fuzzy inference machine is generated. The machine learning system is utilized to perform adjustments and realize supplementation between the neuro network and fuzzy inference [22]. The ANFIS algorithm can also significantly increase prediction accuracy and effectively decrease errors.…”

Section: Anfis Modelmentioning

confidence: 99%

Construction of Color Prediction Model for Damaged Korla Pears during Storage Period

Zhang

Liu

et al. 2023

Applied Sciences

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In this work, to scientifically predict the color of damaged Korla fragrant pears during the storage period with lower economic loss and improved added value of the fragrant pears, eight pericarp color prediction models of damaged Korla fragrant pears during the storage period were established. These models had different membership functions, which were based on the adaptive neuro-fuzzy inference system (ANFIS). The optimal model was chosen and verified. Finally, the pericarp color of fragrant pears was accurately predicted through the degree of damage and storage time. According to the acquired test results, the pericarp brightness (L*) decreased, while both the red–green (a*) and yellow–blue (b*) values increased as the storage time prolonged. In addition, the pericarp color of the damaged fragrant pears during the storage period could be well predicted by using the ANFIS model. More specifically, the model with a membership function of trimf showed the optimal prediction effects of L*, a*, and b* (RMSE = 0.1089, R2 = 0.9773; RMSE = 0.5894, R2 = 0.9853; and RMSE = 0.2360, R2 = 0.9772). Our work provides valuable insights for the prediction of the quality of Korla fragrant pears during the storage period.

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An efficient method for classifying motor imagery using CPSO-trained ANFIS prediction

Cited by 22 publications

References 42 publications

Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

Deep CNN‐based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes

Using ANOVA and ANFİS Approaches in Modelling Agricultural Experiments

Construction of Color Prediction Model for Damaged Korla Pears during Storage Period

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