EEG signal classification based on SVM with improved squirrel search algorithm

Shi, Miao; Wang, Chao; Li, Xian-Zhe; Li, Mingqiang; Wang, Lu; Xie, Nenggang

doi:10.1515/bmt-2020-0038

Cited by 25 publications

(9 citation statements)

References 69 publications

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“…The SVM was used to calculate the recognition rate of motor imagination [ 26 ]. For the SVM-based classification, the “rbf” kernel function was set with a C value as (0.001, 0.01, 0.1, 1); gamma = “auto”; grid research was performed; and the best parameter C was selected through training to determine the optimal classification hyperplane.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

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Motor Imagination of Lower Limb Movements at Different Frequencies

Liu

Chen

Belkacem

et al. 2021

Journal of Healthcare Engineering

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Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by “enhancing the difficulty of imagination tasks,” conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. The results validate the following: the average ERD of HFMI (−1.827) is less than that of LFMI (−1.3487) in the alpha band, so did (−3.4756 < −2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different ( p = 0.0074 < 0.01 ; r = 0.945). The ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. The average recognition rate of HFMI is higher than that of LFMI (87.84% > 76.46%), and the recognition rate of the two modes is significantly different ( p = 0.0034 < 0.01 ; r = 0.429). In summary, this research optimizes the quality of MI brain signal sources by enhancing the difficulty of imagination tasks, achieving the purpose of improving the overall recognition rate of the lower limb MI of the participants and reducing the differences of individual execution effects and signal quality among the subjects.

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Section: Data Analysis and Resultsmentioning

confidence: 99%

“…The SVM is employed to calculate the recognition rate of the two task modes, namely, HFMI and LFMI as follows [ 26 – 29 ]:

…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Motor Imagination of Lower Limb Movements at Different Frequencies

Liu

Chen

Belkacem

et al. 2021

Journal of Healthcare Engineering

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“…In this paper, ensemble learning was used to establish a thermal sensation discrimination model, with a support vector machine (SVM) selected as the unit classifier 27,28 . The SVM is one of the best generalization classifiers in machine learning, which is widely used in physiological signal recognition, especially EEG signal recognition 29–31 . It uses a kernel function to transform samples into a high‐dimensional space and determines the projection direction of a classification plane by maximizing the interval between two classes of data.…”

Section: Methodsmentioning

confidence: 99%

Real‐time regulation of room temperature based on individual thermal sensation using an online brain–computer interface

et al. 2022

Indoor Air

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To maintain desirable indoor temperature is one of the key functions of buildings. On the one hand, indoor temperature control is an important factor affecting building energy consumption. A large proportion of global energy consumption comes from buildings, in which heating, ventilation, and air conditioning (HVAC) systems play an important role. [1][2][3][4] For example, buildings account for 38.9% of the total primary energy use in the United States, of which 34.8% are used for HVAC systems to obtain thermal comfort. 5 On the other hand, the temperature inside a building has an important effect on its occupants. Studies have found that when people experience

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“…The performance is analyzing using Python in keras. Shi et al [6] presented squirrel search algorithm with SVM for efficient EEG classification. To analyze the performance, the braincomputer interface (BCI) competition 2003 dataset III is used which proves that the proposed technique is better than existing.…”

Section: Introductionmentioning

confidence: 99%

Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique

Dalal

Bhairannawar

2022

IJEECS

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Complex <span>modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more </span>effectively.

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EEG signal classification based on SVM with improved squirrel search algorithm

Cited by 25 publications

References 69 publications

Motor Imagination of Lower Limb Movements at Different Frequencies

Motor Imagination of Lower Limb Movements at Different Frequencies

Real‐time regulation of room temperature based on individual thermal sensation using an online brain–computer interface

Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique

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