Cognitive load detection using circulant singular spectrum analysis and Binary Harris Hawks Optimization based feature selection. (January 2023)

• Record Type: Journal Article
• Authors: Yedukondalu, Jammisetty; Sharma, Lakhan Dev
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Cognitive load -- EEG -- C-SSA -- Optimization algorithms: BGWO, BHHO, and BDE -- Machine learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104006
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Cognitive load detection during the mental assignment of neural activity is necessary because it helps to understand the brain's response to stimuli. An electroencephalogram (EEG) can be used to identify cognitive load during mental arithmetic activities. EEG data was collected from public databases such as the mental arithmetic task (MAT) and simultaneous task workload (STEW). In this manuscript, short-term EEG signals were used to detect cognitive load. Circulant singular spectrum analysis (C-SSA) was used to decompose the EEG signals into intrinsic mode functions (IMF's). After that, we extract the entropy based features from the IMF's. For feature selection, optimization algorithms were used, namely Binary Grey Wolf Optimization (BGWO), Binary Harris Hawks Optimization (BHH0), and Binary Differential Evolution (BDE). Furthermore, supervised machine learning methods, namely K-nearest neighbour (KNN) and support vector machine (SVM), were employed to classify selected features based on performance metrics like accuracy (ACC %), sensitivity (SEN), specificity (SPE), precision (PRE), and F-Score (F-S). C-SSA for decomposition and optimization algorithms for feature selection are novel techniques for cognitive load detection. The proposed BHHO-KNN technique achieves the best classification accuracy of 96.88% and 95.28%, individually, for the STEW and MAT datasets. The conclusions of the experiments showed that the proposed technique is more precise at detecting cognitive load compared to existing methods. Highlights: Novel C-SSA is used to decompose the EEG signals of MAT and STEW datasets into IMF's. We extract the entropy-based features from the IMF's. BGWO, BHHO and BDE are used for feature selection to find an optimal feature subset. KNN & SVM used to distance and kernel functions to evaluate the performance metrics. C-SSA＋BHHO＋KNN technique achieves the classification accuracies of 95.28% and 96.88%.