Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis

Antony, Mary Judith; Sankaralingam, Baghavathi Priya; Mahendran, Rakesh Kumar; Gardezi, Akber Abid; Shafiq, Muhammad; Choi, Jin-Ghoo; Hamam, Habib

doi:10.3390/s22197596

Cited by 32 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SVM and general regression neural networks (GRNN) were used for the diagnosis of malfunction [ 39 ]. The adaptive support vector machine (A-SVM) was introduced for classification together with the ORICA-CSP method [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

Zhang

Lin

2023

Sensors

View full text Add to dashboard Cite

Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency.

show abstract

Section: Introductionmentioning

confidence: 99%

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

Zhang

Lin

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The sigmoid function that estimates the posterior class probabilities

v_{j}

is determined using Platt's probabilistic output. An adaptive SVM was chosen because it has been demonstrated to perform much better than the non‐adaptive classifiers when it comes to classification accuracy [22].…”

Section: Methodsmentioning

confidence: 99%

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

A¹,

S²,

Mahendran

et al. 2022

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

Brain–Computer Interfaces (BCIs) based on Electroencephalograms (EEG) monitor mental activity with the ultimate objective of allowing people to communicate with computers only via their thoughts. Users must create precise cerebral activity patterns that the system uses as control signals to do this. A common activity used to elicit such signals is Motor Imagery (MI), in which certain signals are created in the sensorimotor cortex while imagining the movements. The three phases of the traditional EEG–BCI processing pipeline are preprocessing, feature extraction, and classification. We provide categorization advances and track performance gains in 4‐class MI‐based BCIs. In this study, 4‐class MI events are produced via an illusory elevation of the left hand, right hand, feet, and tongue. Finally, a two‐phase classification technique is provided with ANN classifiers being used in the first phase to discriminate between different pair‐wise MI tasks. Secondly, an adaptive SVM classifier is used to assess the user's end task based on the weighted outputs of the classifiers. An adaptive classifier is one technique to maintain consistency in performance, reduce training time, and eliminate non‐stationaries, all of which are required for efficient BCI performance. The suggested approach outperformed conventional two‐stage classification algorithms on MI data, according to experimental findings. The average classification accuracy of this technique is 96% for datasets BCI competition IV 2a. This is a 4% improvement over the comparison approach.

show abstract

“…Antony, M. J., et al (2022) [17] examines the generally utilized non-invasive method for the investigation of motor imagery EEG data. For feature extraction technique, the author uses the Common Spatial Pattern (CSP) and welch transformation method for spectrum estimation and these features are handled through the classi ers.…”

Section: Related Workmentioning

confidence: 99%

Neuro Fuzzy Framework for EEG Signal Classification to improve Supply Chain efficiency

Duy

Minh

et al. 2023

Preprint

View full text Add to dashboard Cite

The Electroencephalogram (EEG), which is used in the diagnosis of brain related disease, which reflects the physiological processes of the human brain. Any human expert must take their time visually examining a large amount of EEG data, and there is a chance that their assessments will differ or be inconsistent across doctors. So, using deep learning techniques, a process of automated identification and diagnosis is started. The Neuro Fuzzy Framework (NFF) classifier is discovered to be the superior classifier when the results are studied and contrasted based on the different factors. This work might make the categorization process stronger in practise, advancing medical diagnosis. The diagnosed information can be further utilized by the supply chain management system for providing the medicines and other needed materials to the user on appropriate time. The enrichment of supply chain management system is analysed in this article.

show abstract

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis

Cited by 32 publications

References 25 publications

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

Two‐phase classification: ANN and A‐SVM classifiers on motor imagery BCI

Neuro Fuzzy Framework for EEG Signal Classification to improve Supply Chain efficiency

Contact Info

Product

Resources

About