Details

Autor(en) / Beteiligte

• Fouziah Md Yassin
• Norita Md Norwawi
• Nor Azila Noh
• Afishah Alias
• Sofina Tamam

Titel

A FUSION OF A DISCRETE WAVELET TRANSFORM-BASED AND TIME-DOMAIN FEATURES EXTRACTION FOR MOTOR IMAGERY CLASSIFICATION

Ist Teil von

• Jordanian journal of computers and information technology (Online), 2024-04, Vol.10 (2), p.108-122

Ort / Verlag

Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)

Erscheinungsjahr

2024

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• A motor imagery (MI)-based brain-computer interface (BCI) has performed successfully as a control mechanism with multiple electroencephalogram (EEG) channels. For practicality, fewer EEG channels are preferable. This paper investigates a single-channel EEG signal for MI. However, there are insufficient features that can be extracted due to a single-channel EEG signal being used in one region of the brain. An effective feature extraction technique plays a critical role in overcoming this limitation. Therefore, this study proposes a fusion of discrete wavelet transform (DWT)-based and time-domain feature extraction to provide more relevant information for classification. The highest accuracy obtained on the BCI Competition III (IVa) dataset is 87.5% with logistic regression (LR) while the OpenBMI dataset attained the highest accuracy of 93% with support vector machine (SVM) as the classifier. Addressing the potential of enhancing the performance of a single EEG channel located on the forehead, the achieved result is relatively promising. [JJCIT 2024; 10(2.000): 108-122]