Skip to main content

The Power Use of Power Spectrum Density for Measures of Cognitive Performance Based on Electroencephalography: Systematic Literature Review

  • Conference paper
  • First Online:
Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics (ICEBEHI 2023)


Humans play more of a role as operators who carry out control functions, so cognitive abilities, especially those related to perception and decision-making, become very important. Cognitive performance can be seen in a person’s ability to complete a cognitive activity. Electroencephalography is a tool for measuring cognitive performance through human brain activity wave signals. The main objective of this article is to systematically review Power Spectrum Density (PSD) feature extraction methods for cognitive performance measurement based on EEG. The methods used in this study are the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) method and Bibliometric analysis using VOSViewer. Data sources totaled 50 articles obtained from Scopus, Science Direct, and IOP Science for the 2013–2023 periods. The results of this article were obtained by observing keywords, density, article trends, feature extraction methods, and the application of EEG. The results showed that out of 50 articles that had been reviewed, 19 used PSD to measure cognitive performance, the Alpha frequency band is the most commonly used in measuring cognitive performance. The increasing use of PSD methods for the measurement of cognitive aspects (fatigue, workload, performance, mental) shows that future research directions can still be developed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions


  1. Karwowski W (2005) Ergonomics and human factors: the paradigms for science, engineering, design, technology and management of human-compatible systems. Ergonomics 48(5):436–463.

    Article  Google Scholar 

  2. Hengki W (2018) Pendidikan Neurosains Dan Implikasinya Dalam Pendidikan Masa Kini. Pendidik Dasar 2(March):1–19

    Google Scholar 

  3. Dehais F, Ayaz H (2018) Progress and direction in neuroergonomics, no 2017. Elsevier

    Google Scholar 

  4. Mehta RK, Parasuraman R (2013) Neuroergonomics: a review of applications to physical and cognitive work. Front Hum Neurosci 7(Dec):1–10.

  5. Parasuraman R, Wilson GF (2008) Putting the brain to work: neuroergonomics past, present, and future. Hum Factors 50(3):468–474.

    Article  Google Scholar 

  6. Jafari MJ, Khosrowabadi R, Khodakarim S, Mohammadian F (2019) The effect of noise exposure on cognitive performance and brain activity patterns. Open Access Maced J Med Sci 7(17):2924–2931.

    Article  Google Scholar 

  7. Cascino GD (1991) Current practice of clinical electroencephalography, 2nd ed, vol 41, no 3

    Google Scholar 

  8. Trad D, Al-Ani T, Monacelli E, Delaplace S, Jemni M (2011) Feature extraction based on empirical mode decomposition and band power approaches for motor imagery tasks classification. In: Proceedings of the IADIS international conference on interfaces human-computer interaction 2011, Part IADIS multi conference on computer science and information systems 2011, MCCSIS 2011, no. April 2015, pp 185–192

    Google Scholar 

  9. Agustina Garcés M, Orosco LL (2018) EEG signal processing in brain-computer interface, 2nd ed. Elsevier B.V.

    Google Scholar 

  10. Kumar N, Kumar J (2016) Measurement of cognitive load in HCI systems using EEG power spectrum: an experimental study. Procedia Comput Sci 84:70–78.

    Article  Google Scholar 

  11. Akrami A, Solhjoo S, Motie-Nasrabadi A, Hashemi-Golpayegani MR (2005) EEG-based mental task classification: linear and nonlinear classification of movement imagery. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society. VOLS, vol 7, pp 4626–4629.

  12. Varsavsky A, Mareels I, Cook M (2011) EPILEPTIC and the EEG EPILEPTIC and the EEG

    Google Scholar 

  13. Moher D et al (2010) CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ 340.

  14. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res 133(April):285–296.

    Article  Google Scholar 

  15. Kästle JL, Anvari B, Krol J, Wurdemann HA (2021) Correlation between situational awareness and EEG signals. Neurocomputing 432:70–79.

    Article  Google Scholar 

  16. Mohamed Z, El Halaby M, Said T, Shawky D, Badawi A (2018) Characterizing focused attention and working memory using EEG. Sensors (Switzerland) 18(11):1–21.

    Article  Google Scholar 

  17. Purnamasari PD, Junika TW (2019) Frequency-based EEG human concentration detection system methods with SVM classification. In: Proceedings: CYBERNETICSCOM 2019 - 2019 IEEE international conference on cybernetics and computational intelligence: towards a smart and human-centered cyber world, pp 29–34.

  18. Pieper K et al (2021) Working with environmental noise and noise-cancelation: a workload assessment With EEG and subjective measures. Front Neurosci 15(November):1–13.

    Article  Google Scholar 

  19. Foy JG, Foy MR (2020) Dynamic changes in EEG power spectral densities during NIH-toolbox Flanker, dimensional change card sort test and episodic memory tests in young adults. Front Hum Neurosci 14(May):1–10.

    Article  Google Scholar 

  20. Zheng Y, Ma Y, Cammon J, Zhang S, Zhang J, Zhang Y (2022) A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm. Comput Biol Med 147(May):105718.

  21. Iqbal MU, Shahab MA, Choudhary M, Srinivasan B, Srinivasan R (2021) Electroencephalography (EEG) based cognitive measures for evaluating the effectiveness of operator training. Process Saf Environ Prot 150:51–67.

    Article  Google Scholar 

  22. Rahman MA, Anjum A, Milu MMH, Khanam F, Uddin MS, Mollah MN (2021) Emotion recognition from EEG-based relative power spectral topography using convolutional neural network. Array 11(June):100072.

  23. Islam M, Ahmed T, Yusuf MSU, Ahmad M (2014) Channel selection and feature extraction for cognitive state estimation with the variation of brain signal. In: 2013 international conference on electrical information and communication technology EICT 2013.

  24. Gentili RJ et al (2018) Combined assessment of attentional reserve and cognitive-motor effort under various levels of challenge with a dry EEG system. Psychophysiology 55(6):1–17.

    Article  Google Scholar 

  25. Wong RZ, Choo YH, Muda AK (2020) Task sensitivity in continuous electroencephalogram person authentication. Int J Adv Comput Sci Appl 11(2):552–558.

    Article  Google Scholar 

  26. Suwandi GRF, Khotimah SN, Suprijadi (2022) Electroencephalography signal power spectral density from measurements in room with and without faraday cage: a comparative study. J Phys Conf Ser 2243(1).

  27. Tseng LH, Cheng MT, Chen ST, Hwang JF, Chen CJ, Chou CY (2013) An EEG investigation of the impact of noise on attention. Adv Mater Res 779:1731–1736.

    Article  Google Scholar 

  28. Arsalan A, Majid M, Butt AR, Anwar SM (2019) Classification of perceived mental stress using a commercially available EEG headband. IEEE J Biomed Heal Inform 23(6):2257–2264.

    Article  Google Scholar 

  29. Abbasi AM, Motamedzade M, Aliabadi M, Golmohammadi R, Tapak L (2018) Study of the physiological and mental health effects caused by exposure to low-frequency noise in a simulated control room. Build Acoust 25(3):233–248.

    Article  Google Scholar 

  30. Ke J, Du J, Luo J (2021) The effect of noise content and level on cognitive performance measured by electroencephalography (EEG). Autom Constr 130.

  31. Dasari D, Shou G, Ding L (2017) ICA-derived EEG correlates to mental fatigue, effort, and workload in a realistically simulated air traffic control task. Front Neurosci 11(May).

  32. Trejo LJ, Kubitz K, Rosipal R, Kochavi RL, Montgomery LD (2015) EEG-based estimation and classification of mental fatigue. Psychology 06(05):572–589.

    Article  Google Scholar 

  33. Ong ZY, Saidatul A, Ibrahim Z (2018) Power spectral density analysis for human EEG-based biometric identification. In: 2018 international conference on computational approach in smart systems design and applications, ICASSDA 2018, no. August, pp 1–6.

  34. Wang S et al (2022) Modulating break types induces divergent low band EEG processes during post-break improvement: a power spectral analysis. Front Hum Neurosci 16.

  35. Jebelli H, Hwang S, Lee SH (2018) EEG-based workers’ stress recognition at construction sites. Autom Constr 93(January):315–324.

    Article  Google Scholar 

  36. Aziezah F, Harke Pratama S, Yulianti, Wahidah S, Haryanto F, Suprijadi (2020) Characterization of individual alpha frequency of EEG signals as an indicator of cognitive fatigue. J Phys Conf Ser 1505(1).

  37. Chen Z, Lin L (2019) Emotional experience evaluation method of interaction task based on EEG technology. IOP Conf Ser Mater Sci Eng 573(1).

  38. Abbasi AM, Motamedzade M, Aliabadi M, Golmohammadi R, Tapak L (2020) Combined effects of noise and air temperature on human neurophysiological responses in a simulated indoor environment. Appl Ergon 88(June):103189.

  39. Iqbal MU, Srinivasan B, Srinivasan R (2020) Dynamic assessment of control room operator’s cognitive workload using electroencephalography (EEG). Comput Chem Eng 141:106726.

    Article  Google Scholar 

  40. Ameera A, Saidatul A, Ibrahim Z (2019) Analysis of EEG spectrum bands using power spectral density for pleasure and displeasure state. IOP Conf Ser Mater Sci Eng 557(1).

  41. Roy S, Islam M, Yusuf MSU, Jahan N (2022) EEG based stress analysis using rhythm specific spectral feature for video game play. Comput Biol Med 148(May):105849.

  42. Kota S, Jasti K, Liu Y, Liu H, Zhang R, Chalak L (2021) EEG spectral power: a proposed physiological biomarker to classify the hypoxic-ischemic encephalopathy severity in real time. Pediatr Neurol 122:7–14.

    Article  Google Scholar 

  43. Lanzone J et al (2021) The effect of Perampanel on EEG spectral power and connectivity in patients with focal epilepsy. Clin Neurophysiol 132(9):2176–2183.

    Article  Google Scholar 

  44. Hou HR, Meng QH (2021) A double-square-based electrode sequence learning method for odor concentration identification using EEG signals. IEEE Trans Instrum Meas 70.

  45. Dirik HB, Darendeli A, Ertan H (2022) The new wireless EEG device Mentalab explore is a valid and reliable system for the measurement of resting state EEG spectral features. Brain Res 1798(November):148164.

  46. Zhi Chin T, Saidatul A, Ibrahim Z (2019) Exploring EEG based authentication for imaginary and non-imaginary tasks using power spectral density method. IOP Conf Ser Mater Sci Eng 557(1).

  47. Thomas KP, Vinod AP (2018) EEG-based biometric authentication using gamma band power during rest state. Circuits Syst Signal Process 37(1):277–289.

    Article  MathSciNet  Google Scholar 

  48. Yang B, Hu C, Wang J, Li B, Wang W (2019) Research on motor imagery EEG modeling based on window optimization and a few channels PSD. In: International conference on digital signal processing, DSP, vol 2018-November, 2019.

  49. Zammouri A et al (2017) Brain waves-based index for workload estimation and mental effort engagement recognition. J Phys Conf Ser 904(1).

  50. Kim C, Sun J, Liu D, Wang Q, Paek S (2018) An effective feature extraction method by power spectral density of EEG signal for 2-class motor imagery-based BCI. Med Biol Eng Comput 56(9):1645–1658.

    Article  Google Scholar 

  51. Touchard C et al (2019) EEG power spectral density under Propofol and its association with burst suppression, a marker of cerebral fragility. Clin Neurophysiol 130(8):1311–1319.

    Article  Google Scholar 

  52. Tang Y, Huang H (2020) An EEG-based brain-computer interface for attention state recognition. In: 2020 international symposium on autonomous systems ISAS 2020, pp 100–104.

  53. Lee H, Kim Y, Park C, Subjects A (2018) Classification of human attention to media lecture. In: International conference on information networking, pp 914–916

    Google Scholar 

  54. Muramatsu T, Washizawa Y, Hiyoshi K (2020) EEG analysis of nursing touch for frustrating work. In: LifeTech 2020—2020 IEEE 2nd global conference on life sciences and technologies, LifeTech, pp 67–71.

  55. Azwar SHNS, Amin MKM, Islam AKMM, Mikami O (2019) Electroencephalogram (EEG) studies on human perception in colours. J Adv Manuf Technol 13(Special Issue 1):163–174

    Google Scholar 

  56. Choi HI, Noh GJ, Shin HC (2020) Measuring the depth of anesthesia using ordinal power spectral density of electroencephalogram. IEEE Access 8:50431–50438.

    Article  Google Scholar 

  57. Jun G, Smitha KG (2017) EEG based stress level identification. In: 2016 IEEE international conference on systems, man, and cybernetics SMC 2016, pp 3270–3274.

  58. Putman P, Verkuil B, Arias-Garcia E, Pantazi I, Van Schie C (2014) EEG theta/beta ratio as a potential biomarker for attentional control and resilience against deleterious effects of stress on attention. Cogn Affect Behav Neurosci 14(2):782–791.

    Article  Google Scholar 

  59. Sadeghian M, Mohammadi Z, Mousavi SM (2021) Investigation of electroencephalography variations of mental workload in the exposure of the psychoacoustic in both male and female groups. Cogn Neurodyn, November 2021.

  60. Sadeghian M et al (2021) Effect of tonal noise and task difficulty on electroencephalography and cognitive performance. Int J Occup Saf Ergon 1–9.

  61. Ke J, Zhang M, Luo X, Chen J (2021) Monitoring distraction of construction workers caused by noise using a wearable electroencephalography (EEG) device. Autom Constr 125(February):103598.

  62. Borghini G et al (2014) Analysis of neurophysiological signals for the training and mental workload assessment of ATCos. In: SIDs 2014 – Proceedings of SESAR innovation days

    Google Scholar 

  63. Chikhi S, Matton N, Blanchet S (2022) EEG power spectral measures of cognitive workload: a meta-analysis. Psychophysiology 59(6):1–24.

    Article  Google Scholar 

  64. Ismail LE, Karwowski W (2020) Applications of EEG indices for the quantification of human cognitive performance: a systematic review and bibliometric analysis. 15(12)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Rahmaniyah Dwi Astuti .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Astuti, R.D., Suhardi, B., Laksono, P.W., Susanto, N., Gaffar, A.R. (2024). The Power Use of Power Spectrum Density for Measures of Cognitive Performance Based on Electroencephalography: Systematic Literature Review. In: Triwiyanto, T., Rizal, A., Caesarendra, W. (eds) Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics. ICEBEHI 2023. Lecture Notes in Electrical Engineering, vol 1182. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-1462-9

  • Online ISBN: 978-981-97-1463-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics