A Cognitive Workload Identification using EEG Power Spectrum
Anshul1, Rashima Mahajan2, Dipali Bansal3
1Anshul, PhD Scholar ECE Department, Manav Rachna International Institute of Research and Studies, Faridabad, India.
2Dipali Bansal, Professor, ECE Department, Manav Rachna International Institute of Research and Studies, Faridabad, India.
3Rashima Mahajan, Associate Professor, ECE Department , Manav Rachna International Institute of Research and Studies, Faridabad, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 8517-8524 | Volume-8 Issue-4, November 2019. | Retrieval Number: C5799098319/2019©BEIESP | DOI: 10.35940/ijrte.C5799.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now a days, Electroencephalography (EEG) is popular to monitor human’s cognitive workload. EEG signals are delicate to the variation in cognitive load in various fields including observing cognitive workload for the intricate environment of military chores. Earlier to acquire the EEG signals high-cost EEG systems were used which bounds their use but now a day’s low-cost headsets are available to capture EEG which makes it a promising set-up to measure cognitive workload. EEGs are initially preprocessed to reflect the artifacts present in it. After preprocessing, signals are ready for further processing. The power spectral density corresponds to the power distribution of EEG signal in the frequency domain which is used to assess the changes in the pattern of the brain. This paper discusses the present progress of research in cognitive workload identification and identifies the techniques associated with the cognitive workload. This proposed research gives the analysis of EEG signal power spectrum density (PSD) during resting state and cognitive workload activities of a human. With power spectral analysis of the EEG signal, seven statistical parameters have been calculated (minimum, maximum, mean, median, mode, standard deviation and range) have been calculated Analysis showed that the in cognitive workload, PSD has significantly changed if compared to the resting state.
Keywords: Electroencephalography, Cognitive workload, Power Spectral Density, Feature Extraction, Statistical Parameters.
Scope of the Article: Cognitive Radio Networks.