A machine learning algorithm for classification of mental tasks. (April 2022)

• Record Type: Journal Article
• Authors: Manoharan, Hariprasath; Abdul Haleem, Sulaima Lebbe; Shitharth, S.; Kshirsagar, Pravin R.; Tirth, Vineet; Thangamani, M.; Chandan, Radha Raman
• Is Part Of: Computers & electrical engineering. Volume 99(2022)
• Publication Date: 2022-04
• Subjects: Brain-computer interface (BCI) -- Electroencephalogram, (EEG) -- Mental task -- Discrete wavelet transform, (DWT) -- B-alert machine -- Classification -- Support vector machine, (SVM) -- Accuracy -- Error -- Artificial neural network, (ANN); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107785
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Classification of mental tasks that exist on cognitive parts of humans is examined using two different methods. In the working model the pre-processing data base is arranged and separated into different sub-bands according to their frequency levels. A classification technique is incorporated with Discrete Wavelet Transform (DWT) for acquiring optimal solutions. Abstract: In this article, a contemporary tack of mental tasks on cognitive parts of humans is appraised using two different approaches such as wavelet transforms at a discrete time (DWT) and support vector machine (SVM). The put forth tack is instilled with the electroencephalogram (EEG) database acquired in real-time from CARE Hospital, Nagpur. Additional data is also acquired from a brain-computer interface (BCI). In the working model, signals from the database are wed out into different frequency sub-bands using DWT. Initially, updated statistical features are obtained from different frequency sub-bands. This type of representation defines the wavelet co-efficient which is introduced for reducing the measurement of data. Then, the projected method is realized using SVM for segregating both port and veracious hand movement. After segregation of EEG signals, results are achieved with an accuracy of 92% for BCI competition paradigm III and 97.89% for B-alert machine. Graphical abstract: Image, graphical abstract