An Efficient Classification of Focal and Non-Focal EEG Signals Using Adaptive DCT Filter Bank

Mehla, Virender Kumar; Singhal, Amit; Singh, Pushpendra

doi:10.1007/s00034-023-02328-z

Cited by 4 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Virender Kumar Mehla et al [16].Having discussed the problem of treating individuals with pharmacoresistant focal epilepsy effectively, the brain's epileptogenic center must be precisely identified. Recently, several machine-learning methods have been created to help neurologists correctly diagnose epileptic patients.…”

Section: Feature Selection Approachmentioning

confidence: 99%

Survey Analysis on smart features selection for machine learning techniques mainly applied to EEG.

Hakem,

Al-Shammary,

Mahdi

2023

JQCM

View full text Add to dashboard Cite

This research presents a survey for analyzing and classifying the EEG signal based on feature selection approaches. Moreover, The increasing complexity of high-dimensional medical datasets necessitates efficient feature selection methods for early disease detection and safeguarding public health. Intelligent feature selection represents an advanced stage in machine learning and innovative computer applications, as it reduces the number of features required for accurate classification. Generally, The main goal of feature selection is to improve the predictive model's performance and reduce the computational cost of modeling. This paper contains a survey of considerable research several on feature selection. The main measures to analyze this paper are Accuracy, precision, Recall, and F1-score assessment. In order to evaluate performance used stander dataset are EEG Bonn University. The results have proven that they have achieved the highest accuracy rate of around 99% compared with different techniques.

show abstract

Section: Feature Selection Approachmentioning

confidence: 99%

Survey Analysis on smart features selection for machine learning techniques mainly applied to EEG.

Hakem,

Al-Shammary,

Mahdi

2023

JQCM

View full text Add to dashboard Cite

show abstract

“…The current research focuses on the exploration of feature extraction algorithms in time, frequency, and space. Time-domain analysis methods mainly through variance, crests and amplitudes, such as autoregressive (AR) [13]. Rodríguez et al [14] analyses the frequency characteristics of a continuous signal by converting it from the time domain to the frequency domain using the Fast Fourier Transform (FFT).…”

Section: Introductionmentioning

confidence: 99%

Stepwise Discriminant Analysis based Optimal Frequency Band Selection and Ensemble Learning for Same Limb MI Recognition

Meng,

Zhu,

et al. 2024

Preprint

View full text Add to dashboard Cite

Same limb motor imagery (MI) brain-computer interfaces can effectively overcome the cognitive disassociation problem of the traditional different-limb MI paradigm, and they can reduce the patient burden and extend the functionality of external devices more effectively. However, the electroencephalogram (EEG) MI features of same limb originate from one side of the brain, which poses a great challenge to MI EEG feature mining and selection as well as accurate decoding. To overcome this problem, we propose an adaptive feature selection strategy for subject-specific optimal frequency band based on regularized common spatial pattern (RCSP) and stepwise discriminant analysis, then combine the integrated classification strategy to accurately decode three types of single-limb MI tasks. As there are minor frequency band differences and huge variability for the same limb MI tasks, the optimal frequency band range for each subject was selected by stepwise discriminant analysis, and RCSP was used to extract spatial distribution features, which reduced the influence of the length of the time window and differences of the frequency bands. Then an integrated classification strategy based on multiple efficient classifiers is used for MI accurate recognition. The proposed method obtains 76.58% accuracy in the unilateral limb MI recognition task, which is 12.67%, 9.89%, 6.62%, and 7.90% higher than other traditional decoding methods such as CSP + LDA, FBCSP + LDA, FBCSP + C2CM, and FBCSP + SVM, respectively. Compared with Deep ConvNet and EEGNet, the decoding accuracy is improved by 16.93% and 7.33%, respectively. The experimental results show that our proposed highly efficient method improves the decoding accuracy for classifying different joints of unilateral limbs and has high promotion and application value.

show abstract

Fourier Decomposition-Based Automated Classification of Healthy, COPD, and Asthma Using Single-Channel Lung Sounds

Koshta,

Kumar Singh,

Behera

et al. 2024

IEEE Trans. Med. Robot. Bionics

View full text Add to dashboard Cite

An Efficient Classification of Focal and Non-Focal EEG Signals Using Adaptive DCT Filter Bank

Cited by 4 publications

References 51 publications

Survey Analysis on smart features selection for machine learning techniques mainly applied to EEG.

Survey Analysis on smart features selection for machine learning techniques mainly applied to EEG.

Stepwise Discriminant Analysis based Optimal Frequency Band Selection and Ensemble Learning for Same Limb MI Recognition

Fourier Decomposition-Based Automated Classification of Healthy, COPD, and Asthma Using Single-Channel Lung Sounds

Contact Info

Product

Resources

About