Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a step towards cognitive brain-computer interface

• Published in: Journal of medical engineering & technology Vol. 46; no. 1; pp. 69 - 77
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 2022
• Subjects: Attention; attentional load; Brain-Computer Interfaces; classification; Cognition; electroencephalograph (EEG); Electroencephalography; Humans; Multiple object tracking (MOT) task; Support Vector Machine; Multiple object tracking (MOT) task; electroencephalograph (EEG); classification; attentional load
• ISSN: 0309-1902; 1464-522X; 1464-522X
• DOI: 10.1080/03091902.2021.1992519

Cognitive brain-computer interface (cBCI) is an emerging area with applications in neurorehabilitation and performance monitoring. cBCI works on the cognitive brain signal that does not require a person to pay much effort unlike the motor brain-computer interface (BCI) however existing cBCI systems currently offer lower accuracy than the motor BCI. Since attention is one of the cognitive signals that can be used to realise the cBCI, this work uses the multiple object tracking (MOT) task to acquire the desired electroencephalograph (EEG) signal from healthy subjects. The main objective of the paper is to explore the preliminary applications of support vector machine (SVM) classifier to classify the attentional load in multiple object tracking task. Results show that the attentional load can be classified using SVM with sensitivity, specificity, and accuracy of 94.03%, 92.50%, and 93.28%, respectively using the spectral entropy EEG feature. The classification performance promises the potential application of the current approach in the cognitive brain-computer interface for neurorehabilitation.