Proceedings Article10.1109/ICCSP48568.2020.9182149
Mental Arithmetic Task Classification using Fourier Decomposition Method
Binish Fatimah,Abhishek Javali,Haaris Ansar,B G Harshitha,Hemant Kumar +4 more
- 28 Jul 2020
- pp 0046-0050
38
TL;DR: A mental arithmetic task detection algorithm from a single lead EEG signal used to decompose the signal into M uniform sub-bands and features, like energy, entropy, and variance, are computed from each of these sub-band.
read more
Abstract: Solving an arithmetic problem is a complex task which involves fact retrieval, memory, sequencing and decision making. Automatic detection of such an activity from EEG signals will help in understanding of brain response to these cognitive tasks. In this work, we propose a mental arithmetic task detection algorithm from a single lead EEG signal. Fourier Decomposition method is used to decompose the signal into M uniform sub-bands and features, like energy, entropy, and variance, are computed from each of these sub-bands. Kruskal-Wallis method has been used to select only the statistically relevant features. These selected features are, then, used to classify the given EEG dataset into two classes using support vector machine with cubic kernel. To validate the efficacy of the proposed algorithm, simulation results are presented using dataset available on MIT PhysioNet, titled EEG during mental arithmetic task.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Efficient detection of myocardial infarction from single lead ECG signal
TL;DR: This work uses single channel electrocardiogram (ECG) signal to develop two automated MI detection algorithms, namely, primary and modified, which perform better than the existing state-of-the-art techniques and have the potential for efficient real-time implementation in MI detection systems.
53
Cognitive load detection using circulant singular spectrum analysis and Binary Harris Hawks Optimization based feature selection
TL;DR: In this article , the authors used the Circulant singular spectrum analysis (C-SSA) to decompose the EEG signals into intrinsic mode functions (IMF) and extracted the entropy based features from the IMF's.
40
Mental arithmetic task load recognition using EEG signal and Bayesian optimized K-nearest neighbor
Lakhan Dev Sharma,Himanshu Chhabra,Urvashi Chauhan,Ritesh Kumar Saraswat,Ramesh Kumar Sunkaria +4 more
TL;DR: An efficient mental load characterization approach using electroencephalogram (EEG) signal and Bayesian optimized K-Nearest Neighbor (BO-KNN) has been proposed in this paper.
31
Automated attention deficit classification system from multimodal physiological signals
TL;DR: The experimental results have revealed that the proposed hybrid classification model could distinguish between an individual’s cases not being attentive and being attentive with accuracy of 88.04% at temporal lobe.
16
References
Neurofeedback Treatment for Attention-Deficit/Hyperactivity Disorder in Children: A Comparison with Methylphenidate
Thomas Fuchs,Niels Birbaumer,Niels Birbaumer,Werner Lutzenberger,John Gruzelier,Jochen Kaiser +5 more
TL;DR: It is suggested that neurofeedback was efficient in improving some of the behavioral concomitants of ADHD in children whose parents favored a nonpharmacological treatment.
Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects.
TL;DR: The hypothesis was tested of whether neurofeedback training (NFT)—applied in order to increase upper alpha but decrease theta power—is capable of increasing cognitive performance, and training success was positively correlated with the improvement in cognitive performance.
Neurophysiological measures of cognitive workload during human-computer interaction
Alan Gevins,Michael E. Smith +1 more
TL;DR: This paper reviews a long-term programme of research aimed at developing cognitive workload monitoring methods based on EEG measures and provides initial evidence for the scientific and technical feasibility of using EEG-based methods for monitoring cognitive load during human–computer interaction.
414
Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks
TL;DR: This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated, and investigates the feasibility of using EEG to allow paralyzed persons to control a device such as a wheelchair.
413
Classification of mental tasks from EEG signals using extreme learning machine.
TL;DR: The study showed that smoothing of the classifiers' outputs can significantly improve their classification accuracies and that ELM needs an order of magnitude less training time and classification accuracy compared with Backpropagation Neural Network and Support Vector Machines.
282