Three-Class Classification of EEG Signals Using Support Vector Machine Methods

Atmaji, Catur; Putra, Agfianto Eko; Tontowi, Irvan Albab

doi:10.1109/icstc.2018.8528610

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…In the next layer, the low-frequency part is decomposed further until desired features are acquired. WT was utilized for extracting EEG features in [30,37,41,43,46,[54][55][56][57][58][59][60][61][62][63][64][65][66]. The original EEG signal was reduced into detail and approximate frequency coefficients.…”

Section: Wavelet Transform (Wt)mentioning

confidence: 99%

“…SVM was also exploited for ERP signal categorization in [66]. EEG signals linked with random words and right and left body movements were classified robustly in [37] using the multiclass SVM approach. The employed SVM provided encouraging outputs with 52.78%, 86.12%, and 96.88% sensitivities for EEG signals linked with random word and right, left body movements.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications

Rasheed

2021

MAKE

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This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs. It investigates the various types of research undertaken in this realm and discusses the role played by ML in performing different BCI tasks. It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal classification, motor imagery categorization, and limb movement classification. This work explores the various methods employed in BCI mechanisms for feature extraction, selection, and classification and provides a comparative study of reviewed methods. This paper assists the readers to gain information regarding the developments made in BCI and ML domains and future improvements needed for improving and designing better BCI applications.

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