Skip to main content
Top
Published in: Arabian Journal for Science and Engineering 3/2024

29-11-2023 | Research Article-Computer Engineering and Computer Science

Subject-Wise Cognitive Load Detection Using Time–Frequency EEG and Bi-LSTM

Authors: Jammisetty Yedukondalu, Diksha Sharma, Lakhan Dev Sharma

Published in: Arabian Journal for Science and Engineering | Issue 3/2024

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Cognitive load detection using electroencephalogram (EEG) signals is a technique employed to understand and measure the mental workload or cognitive demands placed on an individual while performing a task. EEG is a noninvasive method that records fluctuations in brain activity at different cognitive load levels. The publicly available multi-arithmetic task EEG dataset was used. This study introduces a novel approach to detecting cognitive load by utilizing both the 1D-EEG signal and its various time–frequency (T–F) representations as 2D images. The signal underwent preprocessing, including artifact-free segmentation using filters and subsequent normalization, before being fed into a bidirectional long short-term memory (Bi-LSTM) model with different optimizers for classification. It was trained and fine-tuned to achieve high accuracy. Remarkably, our proposed method demonstrates promising performance even with short EEG segments as 4 s. Through 10-fold cross-validation, we achieved an accuracy (Ac%) of 99.55 and 99.88 using 5:5 and 8:2 data splits, respectively. Furthermore, this manuscript includes subject-wise cognitive load detection, providing valuable insights into individual cognitive processes. This approach enables targeted interventions, performance optimization, and mental health monitoring across various domains. For 36 subjects, an average Ac% of 85.22 was attained. Notably, the spectrogram T–F conversion-based 2D image, coupled with a Bi-LSTM classifier and Adam optimizer, outperformed previous state-of-the-art techniques in terms of evaluation metrics.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Paas, F.; Renkl, A.; Sweller, J.: Cognitive load theory and instructional design: recent developments. Educ. Psychol. 38(1), 1–4 (2003)CrossRef Paas, F.; Renkl, A.; Sweller, J.: Cognitive load theory and instructional design: recent developments. Educ. Psychol. 38(1), 1–4 (2003)CrossRef
2.
go back to reference Martin, S.: Measuring cognitive load and cognition: metrics for technology-enhanced learning. Educ. Res. Eval. 20(7–8), 592–621 (2014)CrossRef Martin, S.: Measuring cognitive load and cognition: metrics for technology-enhanced learning. Educ. Res. Eval. 20(7–8), 592–621 (2014)CrossRef
3.
go back to reference Mazher, M.; Aziz, A.A.; Malik, A.S.; Amin, H.U.: An EEG-based cognitive load assessment in multimedia learning using feature extraction and partial directed coherence. IEEE Access 5, 14819–14829 (2017)CrossRef Mazher, M.; Aziz, A.A.; Malik, A.S.; Amin, H.U.: An EEG-based cognitive load assessment in multimedia learning using feature extraction and partial directed coherence. IEEE Access 5, 14819–14829 (2017)CrossRef
4.
go back to reference Mühl, C.; Jeunet, C.; Lotte, F.: EEG-based workload estimation across affective contexts. Front. Neurosci. 8, 114 (2014) Mühl, C.; Jeunet, C.; Lotte, F.: EEG-based workload estimation across affective contexts. Front. Neurosci. 8, 114 (2014)
5.
go back to reference Yin, B.; Chen, F.; Ruiz, N.; Ambikairajah, E.: Speech-based cognitive load monitoring system. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2041–2044. IEEE (2008) Yin, B.; Chen, F.; Ruiz, N.; Ambikairajah, E.: Speech-based cognitive load monitoring system. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2041–2044. IEEE (2008)
6.
go back to reference Lin, C.-T.; King, J.-T.; Fan, J.-W.; Appaji, A.; Prasad, M.: The influence of acute stress on brain dynamics during task switching activities. IEEE Access 6, 3249–3255 (2017) Lin, C.-T.; King, J.-T.; Fan, J.-W.; Appaji, A.; Prasad, M.: The influence of acute stress on brain dynamics during task switching activities. IEEE Access 6, 3249–3255 (2017)
7.
go back to reference Yaribeygi, H.; Panahi, Y.; Sahraei, H.; Johnston, T.P.; Sahebkar, A.: The impact of stress on body function: a review. EXCLI J. 16, 1057 (2017) Yaribeygi, H.; Panahi, Y.; Sahraei, H.; Johnston, T.P.; Sahebkar, A.: The impact of stress on body function: a review. EXCLI J. 16, 1057 (2017)
8.
go back to reference Shi, Y.; Ruiz, N.; Taib, R.; Choi, E.; Chen, F.: Galvanic skin response (GSR) as an index of cognitive load. In: CHI’07 Extended Abstracts on Human Factors in Computing Systems, pp. 2651–2656 (2007) Shi, Y.; Ruiz, N.; Taib, R.; Choi, E.; Chen, F.: Galvanic skin response (GSR) as an index of cognitive load. In: CHI’07 Extended Abstracts on Human Factors in Computing Systems, pp. 2651–2656 (2007)
9.
go back to reference Backs, R.W.; Walrath, L.C.: Eye movement and pupillary response indices of mental workload during visual search of symbolic displays. Appl. Ergon. 23(4), 243–254 (1992)CrossRef Backs, R.W.; Walrath, L.C.: Eye movement and pupillary response indices of mental workload during visual search of symbolic displays. Appl. Ergon. 23(4), 243–254 (1992)CrossRef
10.
go back to reference Thakor, N.V.; Tong, S.: Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng. 6, 453–495 (2004)CrossRef Thakor, N.V.; Tong, S.: Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng. 6, 453–495 (2004)CrossRef
11.
go back to reference Aldayel, M.; Ykhlef, M.; Al-Nafjan, A.: Consumers’ preference recognition based on brain-computer interfaces: advances, trends, and applications. Arab. J. Sci. Eng. 46(9), 8983–8997 (2021)CrossRef Aldayel, M.; Ykhlef, M.; Al-Nafjan, A.: Consumers’ preference recognition based on brain-computer interfaces: advances, trends, and applications. Arab. J. Sci. Eng. 46(9), 8983–8997 (2021)CrossRef
12.
go back to reference Saurabh, S.; Gupta, P.: Deep learning-based modified bidirectional LSTM network for classification of ADHD disorder. Arab. J. Sci. Eng. 1–18 (2023) Saurabh, S.; Gupta, P.: Deep learning-based modified bidirectional LSTM network for classification of ADHD disorder. Arab. J. Sci. Eng. 1–18 (2023)
13.
go back to reference Zhou, Y.; Huang, S.; Xu, Z.; Wang, P.; Wu, X.; Zhang, D.: Cognitive workload recognition using EEG signals and machine learning: a review. IEEE Trans. Cogn. Dev. Syst. (2021) Zhou, Y.; Huang, S.; Xu, Z.; Wang, P.; Wu, X.; Zhang, D.: Cognitive workload recognition using EEG signals and machine learning: a review. IEEE Trans. Cogn. Dev. Syst. (2021)
14.
go back to reference Kaya, Y.; Kuncan, F.; Tekin, R.: A new approach for congestive heart failure and arrhythmia classification using angle transformation with lSTM. Arab. J. Sci. Eng. 47(8), 10497–10513 (2022)CrossRef Kaya, Y.; Kuncan, F.; Tekin, R.: A new approach for congestive heart failure and arrhythmia classification using angle transformation with lSTM. Arab. J. Sci. Eng. 47(8), 10497–10513 (2022)CrossRef
15.
go back to reference Wilson, G.F.: An analysis of mental workload in pilots during flight using multiple psychophysiological measures. Int. J. Aviat. Psychol. 12(1), 3–18 (2002)CrossRef Wilson, G.F.: An analysis of mental workload in pilots during flight using multiple psychophysiological measures. Int. J. Aviat. Psychol. 12(1), 3–18 (2002)CrossRef
16.
go back to reference Wang, Q.; Sourina, O.: Real-time mental arithmetic task recognition from EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 21(2), 225–232 (2013)CrossRef Wang, Q.; Sourina, O.: Real-time mental arithmetic task recognition from EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 21(2), 225–232 (2013)CrossRef
17.
go back to reference Al-Shargie, F.; Tang, T.B.; Badruddin, N.; Kiguchi, M.: Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach. Med. Biol. Eng. Comput. 56, 125–136 (2018)CrossRef Al-Shargie, F.; Tang, T.B.; Badruddin, N.; Kiguchi, M.: Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach. Med. Biol. Eng. Comput. 56, 125–136 (2018)CrossRef
18.
go back to reference Liu, Y.; Lan, Z.; Khoo, H.H. G.; Li, K.H.H.; Sourina, O.; Mueller-Wittig, W.: EEG-based evaluation of mental fatigue using machine learning algorithms. In: 2018 International Conference on Cyberworlds (CW), pp. 276–279. IEEE (2018) Liu, Y.; Lan, Z.; Khoo, H.H. G.; Li, K.H.H.; Sourina, O.; Mueller-Wittig, W.: EEG-based evaluation of mental fatigue using machine learning algorithms. In: 2018 International Conference on Cyberworlds (CW), pp. 276–279. IEEE (2018)
19.
go back to reference Saadati, M.; Nelson, J.; Ayaz, H.: Convolutional neural network for hybrid fnirs-EEG mental workload classification. In: Advances in Neuroergonomics and Cognitive Engineering: Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, July 24–28, 2019, Washington DC, USA, vol. 10, pp. 221–232. Springer (2020) Saadati, M.; Nelson, J.; Ayaz, H.: Convolutional neural network for hybrid fnirs-EEG mental workload classification. In: Advances in Neuroergonomics and Cognitive Engineering: Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, July 24–28, 2019, Washington DC, USA, vol. 10, pp. 221–232. Springer (2020)
20.
go back to reference Kuanar, S.; Athitsos, V.; Pradhan, N.; Mishra, A.; Rao, K. R.: Cognitive analysis of working memory load from EEG, by a deep recurrent neural network. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2576–2580. IEEE (2018) Kuanar, S.; Athitsos, V.; Pradhan, N.; Mishra, A.; Rao, K. R.: Cognitive analysis of working memory load from EEG, by a deep recurrent neural network. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2576–2580. IEEE (2018)
21.
go back to reference Malviya, L.; Mal, S.: A novel technique for stress detection from EEG signal using hybrid deep learning model. Neural Comput. Appl. 34(22), 19819–19830 (2022)CrossRef Malviya, L.; Mal, S.: A novel technique for stress detection from EEG signal using hybrid deep learning model. Neural Comput. Appl. 34(22), 19819–19830 (2022)CrossRef
22.
go back to reference Zhang, P.; Wang, X.; Zhang, W.; Chen, J.: Learning spatial-spectral-temporal EEG features with recurrent 3d convolutional neural networks for cross-task mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27(1), 31–42 (2018)CrossRef Zhang, P.; Wang, X.; Zhang, W.; Chen, J.: Learning spatial-spectral-temporal EEG features with recurrent 3d convolutional neural networks for cross-task mental workload assessment. IEEE Trans. Neural Syst. Rehabil. Eng. 27(1), 31–42 (2018)CrossRef
23.
go back to reference Zhong, Y.; Jianhua, Z.: Cross-subject classification of mental fatigue by neurophysiological signals and ensemble deep belief networks. In: 36th Chinese Control Conference (CCC), pp. 10966–10971. IEEE (2017) Zhong, Y.; Jianhua, Z.: Cross-subject classification of mental fatigue by neurophysiological signals and ensemble deep belief networks. In: 36th Chinese Control Conference (CCC), pp. 10966–10971. IEEE (2017)
24.
go back to reference Budak, U.; Bajaj, V.; Akbulut, Y.; Atila, O.; Sengur, A.: An effective hybrid model for EEG-based drowsiness detection. IEEE Sens. J. 19(17), 7624–7631 (2019)ADSCrossRef Budak, U.; Bajaj, V.; Akbulut, Y.; Atila, O.; Sengur, A.: An effective hybrid model for EEG-based drowsiness detection. IEEE Sens. J. 19(17), 7624–7631 (2019)ADSCrossRef
25.
go back to reference Malviya, L.; Mal, S.: CIS feature selection based dynamic ensemble selection model for human stress detection from EEG signals. Cluster Computing 1–15 (2023) Malviya, L.; Mal, S.: CIS feature selection based dynamic ensemble selection model for human stress detection from EEG signals. Cluster Computing 1–15 (2023)
26.
go back to reference Yenurkar, G.; Mal, S.: Future forecasting prediction of covid-19 using hybrid deep learning algorithm. Multimed. Tools Appl. 82(15), 22497–22523 (2023)CrossRef Yenurkar, G.; Mal, S.: Future forecasting prediction of covid-19 using hybrid deep learning algorithm. Multimed. Tools Appl. 82(15), 22497–22523 (2023)CrossRef
27.
go back to reference Yenurkar, G.K.; Mal, S.; Nyangaresi, V.O.; Hedau, A.; Hatwar, P.; Rajurkar, S.; Khobragade, J.: Multifactor data analysis to forecast an individual’s severity over novel COVID-19 pandemic using extreme gradient boosting and random forest classifier algorithms. Eng. Rep. e12678 (2023) Yenurkar, G.K.; Mal, S.; Nyangaresi, V.O.; Hedau, A.; Hatwar, P.; Rajurkar, S.; Khobragade, J.: Multifactor data analysis to forecast an individual’s severity over novel COVID-19 pandemic using extreme gradient boosting and random forest classifier algorithms. Eng. Rep. e12678 (2023)
28.
go back to reference Yenurkar, G.K.; Mal, S.: Effective detection of COVID-19 outbreak in chest x-rays using fusionnet model. Imaging Sci. J. 70(8), 535–555 (2022)CrossRef Yenurkar, G.K.; Mal, S.: Effective detection of COVID-19 outbreak in chest x-rays using fusionnet model. Imaging Sci. J. 70(8), 535–555 (2022)CrossRef
29.
go back to reference Roy, B.; Malviya, L.; Kumar, R.; Mal, S.; Kumar, A.; Bhowmik, T.; Hu, J.W.: Hybrid deep learning approach for stress detection using decomposed EEG signals. Diagnostics 13(11), 1936 (2023)CrossRef Roy, B.; Malviya, L.; Kumar, R.; Mal, S.; Kumar, A.; Bhowmik, T.; Hu, J.W.: Hybrid deep learning approach for stress detection using decomposed EEG signals. Diagnostics 13(11), 1936 (2023)CrossRef
30.
go back to reference Zyma, I.; Tukaev, S.; Seleznov, I.; Kiyono, K.; Popov, A.; Chernykh, M.; Shpenkov, O.: Electroencephalograms during mental arithmetic task performance. Data 4(1), 14 (2019)CrossRef Zyma, I.; Tukaev, S.; Seleznov, I.; Kiyono, K.; Popov, A.; Chernykh, M.; Shpenkov, O.: Electroencephalograms during mental arithmetic task performance. Data 4(1), 14 (2019)CrossRef
31.
go back to reference Sairamya, N.; Susmitha, L.; George, S.T.; Subathra, M.: Hybrid approach for classification of electroencephalographic signals using time–frequency images with wavelets and texture features. In: Intelligent Data Analysis for Biomedical Applications, pp. 253–273. Elsevier (2019). Sairamya, N.; Susmitha, L.; George, S.T.; Subathra, M.: Hybrid approach for classification of electroencephalographic signals using time–frequency images with wavelets and texture features. In: Intelligent Data Analysis for Biomedical Applications, pp. 253–273. Elsevier (2019).
32.
go back to reference Boashash, B.: Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals. Proc. IEEE 80(4), 520–538 (1992)ADSCrossRef Boashash, B.: Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals. Proc. IEEE 80(4), 520–538 (1992)ADSCrossRef
33.
go back to reference Vakkuri, A.; Yli-Hankala, A.; Talja, P.; Mustola, S.; Tolvanen-Laakso, H.; Sampson, T.; Viertiö-Oja, H.: Time–frequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia. Acta Anaesthesiol. Scand. 48(2), 145–153 (2004)CrossRef Vakkuri, A.; Yli-Hankala, A.; Talja, P.; Mustola, S.; Tolvanen-Laakso, H.; Sampson, T.; Viertiö-Oja, H.: Time–frequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia. Acta Anaesthesiol. Scand. 48(2), 145–153 (2004)CrossRef
34.
go back to reference Hosny, M.; Zhu, M.; Gao, W.; Fu, Y.: A novel deep LSTM network for artifacts detection in microelectrode recordings. Biocybern. Biomed. Eng. 40(3), 1052–1063 (2020)CrossRef Hosny, M.; Zhu, M.; Gao, W.; Fu, Y.: A novel deep LSTM network for artifacts detection in microelectrode recordings. Biocybern. Biomed. Eng. 40(3), 1052–1063 (2020)CrossRef
35.
go back to reference Bhanuse, R.S.; Mal, S.: Optimal e-learning course recommendation with sentiment analysis using hybrid similarity framework. Multimed. Tools Appl. 1–30 (2023) Bhanuse, R.S.; Mal, S.: Optimal e-learning course recommendation with sentiment analysis using hybrid similarity framework. Multimed. Tools Appl. 1–30 (2023)
36.
go back to reference Altuve, M.; Lizarazo, P.; Villamizar, J.: Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks. Biocybern. Biomed. Eng. 40(3), 901–909 (2020)CrossRef Altuve, M.; Lizarazo, P.; Villamizar, J.: Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks. Biocybern. Biomed. Eng. 40(3), 901–909 (2020)CrossRef
37.
go back to reference Yildirim, Ö.: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 96, 189–202 (2018)CrossRef Yildirim, Ö.: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 96, 189–202 (2018)CrossRef
38.
go back to reference Sharma, A.; Garg, N.; Patidar, S.; Tan, R.S.; Acharya, U.R.: Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM. Comput. Biol. Med. 120, 103753 (2020)CrossRef Sharma, A.; Garg, N.; Patidar, S.; Tan, R.S.; Acharya, U.R.: Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM. Comput. Biol. Med. 120, 103753 (2020)CrossRef
39.
go back to reference Priya, T.H.; Mahalakshmi, P.; Naidu, V.; Srinivas, M.: Stress detection from EEG using power ratio. In: International Conference on Emerging Trends in Information Technology and Engineering (IC-ETITE). IEEE, pp 1–6 (2020) Priya, T.H.; Mahalakshmi, P.; Naidu, V.; Srinivas, M.: Stress detection from EEG using power ratio. In: International Conference on Emerging Trends in Information Technology and Engineering (IC-ETITE). IEEE, pp 1–6 (2020)
40.
go back to reference Subhani, A.R.; Mumtaz, W.; Saad, M.N.B.M.; Kamel, N.; Malik, A.S.: Machine learning framework for the detection of mental stress at multiple levels. IEEE Access 5, 13545–13556 (2017)CrossRef Subhani, A.R.; Mumtaz, W.; Saad, M.N.B.M.; Kamel, N.; Malik, A.S.: Machine learning framework for the detection of mental stress at multiple levels. IEEE Access 5, 13545–13556 (2017)CrossRef
41.
go back to reference Al-Shargie, F.; Tang, T.B.; Badruddin, N.; Kiguchi, M.: Mental stress quantification using EEG signals. In: International Conference for Innovation in Biomedical Engineering and Life Sciences: ICIBEL2015, 6–8 December 2015, Putrajaya, Malaysia 1, pp. 15–19. Springer (2016) Al-Shargie, F.; Tang, T.B.; Badruddin, N.; Kiguchi, M.: Mental stress quantification using EEG signals. In: International Conference for Innovation in Biomedical Engineering and Life Sciences: ICIBEL2015, 6–8 December 2015, Putrajaya, Malaysia 1, pp. 15–19. Springer (2016)
42.
go back to reference Sharma, L.D.; Saraswat, R.K.; Sunkaria, R.K.: Cognitive performance detection using entropy-based features and lead-specific approach. SIViP 15(8), 1821–1828 (2021)CrossRef Sharma, L.D.; Saraswat, R.K.; Sunkaria, R.K.: Cognitive performance detection using entropy-based features and lead-specific approach. SIViP 15(8), 1821–1828 (2021)CrossRef
43.
go back to reference Dehzangi, O.; Sahu, V.; Rajendra, V.; Taherisadr, M.: GSR-based distracted driving identification using discrete & continuous decomposition and wavelet packet transform. Smart Health 14, 100085 (2019)CrossRef Dehzangi, O.; Sahu, V.; Rajendra, V.; Taherisadr, M.: GSR-based distracted driving identification using discrete & continuous decomposition and wavelet packet transform. Smart Health 14, 100085 (2019)CrossRef
44.
go back to reference Cheema, A.; Singh, M.: Psychological stress detection using phonocardiography signal: An empirical mode decomposition approach. Biomed. Signal Process. Control 49, 493–505 (2019)CrossRef Cheema, A.; Singh, M.: Psychological stress detection using phonocardiography signal: An empirical mode decomposition approach. Biomed. Signal Process. Control 49, 493–505 (2019)CrossRef
45.
go back to reference Yedukondalu, J.; Sharma, L. D.: Cognitive load detection using binary salp swarm algorithm for feature selection. In: 2022 IEEE 6th Conference on Information and Communication Technology (CICT), pp. 1–5. IEEE (2022) Yedukondalu, J.; Sharma, L. D.: Cognitive load detection using binary salp swarm algorithm for feature selection. In: 2022 IEEE 6th Conference on Information and Communication Technology (CICT), pp. 1–5. IEEE (2022)
46.
go back to reference Yedukondalu, J.; Sharma, L.D.: Cognitive load detection using circulant singular spectrum analysis and binary Harris Hawks optimization based feature selection. Biomed. Signal Process. Control 79, 104006 (2023)CrossRef Yedukondalu, J.; Sharma, L.D.: Cognitive load detection using circulant singular spectrum analysis and binary Harris Hawks optimization based feature selection. Biomed. Signal Process. Control 79, 104006 (2023)CrossRef
47.
go back to reference Sharma, L.D.; Bohat, V.K.; Habib, M.; Ala’M, A.-Z.; Faris, H.; Aljarah, I.: Evolutionary inspired approach for mental stress detection using EEG signal. Expert Syst. Appl. 197, 116634 (2022)CrossRef Sharma, L.D.; Bohat, V.K.; Habib, M.; Ala’M, A.-Z.; Faris, H.; Aljarah, I.: Evolutionary inspired approach for mental stress detection using EEG signal. Expert Syst. Appl. 197, 116634 (2022)CrossRef
48.
go back to reference Gupta, R.; Alam, M.A.; Agarwal, P.: Modified support vector machine for detecting stress level using EEG signals. Comput. Intell. Neurosci. 2020, 1–14 (2020)CrossRef Gupta, R.; Alam, M.A.; Agarwal, P.: Modified support vector machine for detecting stress level using EEG signals. Comput. Intell. Neurosci. 2020, 1–14 (2020)CrossRef
49.
go back to reference Yedukondalu, J.; Sharma, L.D.: Cognitive load detection using Ci-SSA for EEG signal decomposition and nature-inspired feature selection. Turk. J. Electr. Eng. Comput. Sci. 31(5), 771–791 (2023)CrossRef Yedukondalu, J.; Sharma, L.D.: Cognitive load detection using Ci-SSA for EEG signal decomposition and nature-inspired feature selection. Turk. J. Electr. Eng. Comput. Sci. 31(5), 771–791 (2023)CrossRef
50.
go back to reference Dyer, C.; Ballesteros, M.; Ling, W.; Matthews, A.; Smith, N.A.: Transition-based dependency parsing with stack long short-term memory, arXiv preprint arXiv:1505.08075 (2015) Dyer, C.; Ballesteros, M.; Ling, W.; Matthews, A.; Smith, N.A.: Transition-based dependency parsing with stack long short-term memory, arXiv preprint arXiv:​1505.​08075 (2015)
51.
go back to reference Hochreiter, S.; Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef Hochreiter, S.; Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
52.
go back to reference Graves, A.; Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)CrossRef Graves, A.; Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)CrossRef
53.
go back to reference Zhao, J.; Mao, X.; Chen, L.: Speech emotion recognition using deep 1D & 2D CNN LSTM networks. Biomed. Signal Process. Control 47, 312–323 (2019)CrossRef Zhao, J.; Mao, X.; Chen, L.: Speech emotion recognition using deep 1D & 2D CNN LSTM networks. Biomed. Signal Process. Control 47, 312–323 (2019)CrossRef
54.
go back to reference Ganguly, B.; Chatterjee, A.; Mehdi, W.; Sharma, S.; Garai, S.: EEG based mental arithmetic task classification using a stacked long short term memory network for brain–computer interfacing. In: IEEE VLSI Device Circuit and System (VLSI DCS), pp. 89–94. IEEE (2020) Ganguly, B.; Chatterjee, A.; Mehdi, W.; Sharma, S.; Garai, S.: EEG based mental arithmetic task classification using a stacked long short term memory network for brain–computer interfacing. In: IEEE VLSI Device Circuit and System (VLSI DCS), pp. 89–94. IEEE (2020)
55.
go back to reference Goenka, U.; Patil, P.; Gosalia, K.; Jagetia, A.: Classification of electroencephalograms during mathematical calculations using deep learning. In: 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI), pp. 12–17. IEEE (2022) Goenka, U.; Patil, P.; Gosalia, K.; Jagetia, A.: Classification of electroencephalograms during mathematical calculations using deep learning. In: 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI), pp. 12–17. IEEE (2022)
56.
go back to reference Saini, M.; Satija, U.; Upadhayay, M.D.: DSCNN-CAU: deep-learning-based mental activity classification for IoT implementation toward portable BCI. IEEE Internet Things J. 10(10), 8944–8957 (2022)CrossRef Saini, M.; Satija, U.; Upadhayay, M.D.: DSCNN-CAU: deep-learning-based mental activity classification for IoT implementation toward portable BCI. IEEE Internet Things J. 10(10), 8944–8957 (2022)CrossRef
57.
go back to reference Sundaresan, A.; Penchina, B.; Cheong, S.; Grace, V.; Valero-Cabré, A.; Martel, A.: Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI. Brain Inform. 8(1), 1–12 (2021) Sundaresan, A.; Penchina, B.; Cheong, S.; Grace, V.; Valero-Cabré, A.; Martel, A.: Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI. Brain Inform. 8(1), 1–12 (2021)
58.
go back to reference Chakladar, D.D.; Dey, S.; Roy, P.P.; Dogra, D.P.: EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm. Biomed. Signal Process. Control 60, 101989 (2020) Chakladar, D.D.; Dey, S.; Roy, P.P.; Dogra, D.P.: EEG-based mental workload estimation using deep BLSTM-LSTM network and evolutionary algorithm. Biomed. Signal Process. Control 60, 101989 (2020)
59.
go back to reference Bhatnagar, S.; Khandelwal, S.; Jain, S.; Vyawahare, H.: A deep learning approach for assessing stress levels in patients using electroencephalogram signals. Decis. Anal. J. 7, 100211 (2023)CrossRef Bhatnagar, S.; Khandelwal, S.; Jain, S.; Vyawahare, H.: A deep learning approach for assessing stress levels in patients using electroencephalogram signals. Decis. Anal. J. 7, 100211 (2023)CrossRef
60.
go back to reference Yoo, G.; Kim, H.; Hong, S.: Prediction of cognitive load from electroencephalography signals using long short-term memory network. Bioengineering 10(3), 361 (2023) Yoo, G.; Kim, H.; Hong, S.: Prediction of cognitive load from electroencephalography signals using long short-term memory network. Bioengineering 10(3), 361 (2023)
61.
go back to reference Mughal, N.E.; Khan, M.J.; Khalil, K.; Javed, K.; Sajid, H.; Naseer, N.; Ghafoor, U.; Hong,K.-S.: EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM. Front. Neurorobot. (2022) Mughal, N.E.; Khan, M.J.; Khalil, K.; Javed, K.; Sajid, H.; Naseer, N.; Ghafoor, U.; Hong,K.-S.: EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM. Front. Neurorobot. (2022)
62.
go back to reference Kang, M.; Shin, S.; Jung, J.; Kim, Y.T.: Classification of mental stress using CNN-LSTM algorithms with electrocardiogram signals. J. Healthc. Eng. 2021, 1–11 (2021) Kang, M.; Shin, S.; Jung, J.; Kim, Y.T.: Classification of mental stress using CNN-LSTM algorithms with electrocardiogram signals. J. Healthc. Eng. 2021, 1–11 (2021)
Metadata
Title
Subject-Wise Cognitive Load Detection Using Time–Frequency EEG and Bi-LSTM
Authors
Jammisetty Yedukondalu
Diksha Sharma
Lakhan Dev Sharma
Publication date
29-11-2023
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 3/2024
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-023-08494-1

Other articles of this Issue 3/2024

Arabian Journal for Science and Engineering 3/2024 Go to the issue

Research Article-Computer Engineering and Computer Science

A Novel Cyber Security Model Using Deep Transfer Learning

Research Article-Computer Engineering and Computer Science

LightFIDS: Lightweight and Hierarchical Federated IDS for Massive IoT in 6G Network

Research Article-Computer Engineering and Computer Science

Blockchain Based n-party Virtual Payment Model with Concurrent Execution

Premium Partners