Skip to main content
SpringerLink
Log in

On effective cognitive state classification using novel feature extraction strategies

  • Research Article
  • Published:
Cognitive Neurodynamics Aims and scope Submit manuscript

Abstract

Investigating new features for human cognitive state classification is an intiguing area of research with Electroencephalography (EEG) based signal analysis. We plan to develop a cost-effective system for cognitive state classification using ambulatory EEG signals. A novel event driven environment is created using external stimuli for capturing EEG data using a 14-channel Emotiv neuro-headset. A new feature extraction method, Gammatone Cepstrum Coefficients (GTCC) is introduced for ambulatory EEG signal analysis. The efficacy of this technique is compared with other feature extraction methods such as Discrete Wavelet Transformation (DWT) and Mel−Frequency Cepstral Coefficients (MFCC) using statistical metrics such as Fisher Discriminant Ratio (FDR) and Logistic Regression (LR). We obtain higher values for GTCC features, demonstrating its discriminative power during classification. A superior performance is achieved for the EEG dataset with a novel ensemble feature space comprising of GTCC and MFCC. Furthermore, the ensemble feature sets are passed through a proposed 1D Convolution Neural Networks (CNN) model to extract novel features. Various classification models like Probabilistic neural network (P-NN), Linear Discriminant Analysis (LDA), Multi-Class Support Vector Machine (MCSVM), Decision Tree (DT), Random Forest (RF) and Deep Convolutional Generative Adversarial Network (DCGAN) are employed to observe best accuracy on extracted features. The proposed GTCC, (GTCC+MFCC) & (GTCC +MFCC +CNN) features outperform the state-of-the-art techniques for all cases in our work. With GTCC+MFCC feature space and GTCC+MFCC+CNN features, accuracies of 96.42% and 96.14% are attained with the DCGAN classifier. Higher classification accuracies of the proposed system makes it a cynosure in the field of cognitive science.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39
Fig. 40
Fig. 41

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Abdulla WH (2002) Auditory based feature vectors for speech recognition systems. Adv Commun Softw Technol pp 231–236

  • Agarwal R, Gotman J (2001) Computer-assisted sleep staging. IEEE Trans Biomed Eng 48(12):1412–1423

    Article  CAS  Google Scholar 

  • Albayrak M (2009) The detection of an epileptiform activity on eeg signals by using data mining process. Technol Appl Sci 4(1):1–12

    Google Scholar 

  • Amores J, Benavides X, Maes P (2016) Psychicvr: Increasing mindfulness by using virtual reality and brain computer interfaces. In: Proceedings of the 2016 CHI conference extended abstracts on human factors in computing systems, pp 2–2

  • Bajaj V, Pachori RB (2013) Automatic classification of sleep stages based on the time-frequency image of eeg signals. Comput Methods Programs Biomed 112(3):320–328

    Article  Google Scholar 

  • Bao FS, Lie DYC, Zhang Y (2008) A new approach to automated epileptic diagnosis using eeg and probabilistic neural network. In: 2008 20th IEEE international conference on tools with artificial intelligence, IEEE, vol 2, pp 482–486

  • Bashivan P, Rish I, Heisig S (2016) Mental state recognition via wearable eeg. arXiv preprint arXiv:160200985

  • Bruzzo AA, Gesierich B, Santi M, Tassinari CA, Birbaumer N, Rubboli G (2008) Permutation entropy to detect vigilance changes and preictal states from scalp eeg in epileptic patients a preliminary study. Neurol Sci 29(1):3–9

    Article  Google Scholar 

  • Castellanos NP, Makarov VA (2006) Recovering eeg brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods 158(2):300–312

    Article  Google Scholar 

  • Cheng O, Abdulla W, Salcic Z (2005) Performance evaluation of front-end algorithms for robust speech recognition. In: Proceedings of the eighth international symposium on signal processing and its applications, 2005, IEEE, vol 2, pp 711–714

  • Cowling M, Sitte R (2003) Comparison of techniques for environmental sound recognition. Pattern Recogn Lett 24(15):2895–2907

    Article  Google Scholar 

  • D’Alessandro M, Vachtsevanos G, Hinson A, Esteller R, Echauz J, Litt B (2001) A genetic approach to selecting the optimal feature for epileptic seizure prediction. In: 2001 Conference proceedings of the 23rd annual international conference of the ieee engineering in medicine and biology society, IEEE, vol 2, pp 1703–1706

  • Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 36(5):961–1005

    Article  Google Scholar 

  • Dimoulas C, Kalliris G, Papanikolaou G, Kalampakas A (2007) Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: a bioacoustics application in gastrointestinal-motility monitoring. Comput Biol Med 37(4):438–462

    Article  CAS  Google Scholar 

  • Dutta S, Hazra S, Nandy A (2019) Human cognitive state classification through ambulatory eeg signal analysis. In: International conference on artificial intelligence and soft computing, pp 169–181, Springer, Berlin

  • Eronen AJ, Peltonen VT, Tuomi JT, Klapuri AP, Fagerlund S, Sorsa T, Lorho G, Huopaniemi J (2005) Audio-based context recognition. IEEE Trans Audio Speech Lang Process 14(1):321–329

    Article  Google Scholar 

  • Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H (2012) Automated sleep stage identification system based on time-frequency analysis of a single eeg channel and random forest classifier. Comput Methods Programs Biomed 108(1):10–19

    Article  Google Scholar 

  • Gajbhiye P, Mingchinda N, Chen W, Mukhopadhyay SC, Wilaiprasitporn T, Tripathy RK (2020) Wavelet domain optimized savitzky-golay filter for the removal of motion artifacts from eeg recordings. IEEE Trans Instrum Meas 70:1–11

    Article  Google Scholar 

  • Geng S, Zhou W, Yuan Q, Cai D, Zeng Y (2011) Eeg non-linear feature extraction using correlation dimension and hurst exponent. Neurol Res 33(9):908–912

    Article  Google Scholar 

  • Glavinovitch A, Swamy M, Plotkin E (2005) Wavelet-based segmentation techniques in the detection of microarousals in the sleep eeg. In: 48th Midwest symposium on circuits and systems, 2005, IEEE, pp 1302–1305

  • Harshavarthini S, Aswathy M, Harshini P, Priyanka G (2019) Automated epileptic seizures detection and classification. Int J Sci Res Comput Sci Eng Inf Technol 5(1):555–560

    Google Scholar 

  • Jahankhani P, Kodogiannis V, Revett K (2006) Eeg signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 international symposium on modern computing (JVA’06), IEEE, pp 120–124

  • Kandel ER, Schwartz JH, Jessell TM, of Biochemistry D, Jessell MBT, Siegelbaum S, Hudspeth A (2000) Principles of neural science, vol 4. McGraw-hill New York

  • Kannathal N, Choo ML, Acharya UR, Sadasivan P (2005) Entropies for detection of epilepsy in eeg. Comput Methods Programs Biomed 80(3):187–194

    Article  CAS  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  • Lajnef T, Chaibi S, Ruby P, Aguera PE, Eichenlaub JB, Samet M, Kachouri A, Jerbi K (2015) Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. J Neurosci Methods 250:94–105

    Article  Google Scholar 

  • Lakhan P, Banluesombatkul N, Changniam V, Dhithijaiyratn R, Leelaarporn P, Boonchieng E, Hompoonsup S, Wilaiprasitporn T (2019) Consumer grade brain sensing for emotion recognition. IEEE Sens J 19(21):9896–9907

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  Google Scholar 

  • Mohseni HR, Maghsoudi A, Shamsollahi MB (2006) Seizure detection in eeg signals: a comparison of different approaches. In: 2006 International conference of the IEEE engineering in medicine and biology society, IEEE, pp 6724–6727

  • Newland DE (2012) An introduction to random vibrations, spectral and wavelet analysis. Courier Corporation

  • Nixon M, Aguado A (2019) Feature extraction and image processing for computer vision. Academic press, Cambridge

    Google Scholar 

  • Oropesa E, Cycon HL, Jobert M (1999) Sleep stage classification using wavelet transform and neural network. International computer science institute

  • Pan ST, Kuo CE, Zeng JH, Liang SF (2012) A transition-constrained discrete hidden markov model for automatic sleep staging. Biomed Eng Online 11(1):52

    Article  Google Scholar 

  • Petrosian A, Prokhorov D, Homan R, Dasheiff R, Wunsch D II (2000) Recurrent neural network based prediction of epileptic seizures in intra-and extracranial eeg. Neurocomputing 30(1–4):201–218

    Article  Google Scholar 

  • Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, Johnson HJ, Paulsen JS, Turner JA, Calhoun VD (2014) Deep learning for neuroimaging: a validation study. Front Neurosci 8:229

    Article  Google Scholar 

  • Pradhan N, Sadasivan P, Arunodaya G (1996) Detection of seizure activity in eeg by an artificial neural network: A preliminary study. Comput Biomed Res 29(4):303–313

    Article  CAS  Google Scholar 

  • Procházka A, Jech J, Smith J (1994) Wavelet transform use in signal processing. In: 31st International conference in acoustics, pp 209–213

  • Qayyum A, Khan MA, Mazher M, Suresh M (2018) Classification of eeg learning and resting states using 1d-convolutional neural network for cognitive load assesment. In: 2018 IEEE student conference on research and development (SCOReD), IEEE, pp 1–5

  • Rabaoui A, Lachiri Z, Ellouze N (2007) Towards an optimal feature set for robustness improvement of sounds classification in a hmm-based classifier adapted to real world background noise. In: Proceedings of the 4th International Multi-Conference on Systems, Signals and Devices

  • Radha M, Garcia-Molina G, Poel M, Tononi G (2014) Comparison of feature and classifier algorithms for online automatic sleep staging based on a single eeg signal. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 1876–1880

  • Sanei S, Chambers JA (2013) EEG signal processing. John Wiley and Sons, New Jersey

    Google Scholar 

  • Sawangjai P, Hompoonsup S, Leelaarporn P, Kongwudhikunakorn S, Wilaiprasitporn T (2019) Consumer grade eeg measuring sensors as research tools: a review. IEEE Sens J 20(8):3996–4024

    Article  Google Scholar 

  • Schluter R, Bezrukov I, Wagner H, Ney H (2007) Gammatone features and feature combination for large vocabulary speech recognition. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07, IEEE, vol 4, pp IV–649

  • Selesnick IW, Baraniuk RG, Kingsbury NC (2005) The dual-tree complex wavelet transform. IEEE Signal Process Mag 22(6):123–151

    Article  Google Scholar 

  • Şen B, Peker M (2013) Novel approaches for automated epileptic diagnosis using fcbf selection and classification algorithms. Turk J Electr Eng Comput Sci 21(Sup 1):2092–2109

    Article  Google Scholar 

  • Şen B, Peker M, Çavuşoğlu A, Çelebi FV (2014) A comparative study on classification of sleep stage based on eeg signals using feature selection and classification algorithms. J Med Syst 38(3):18

    Article  Google Scholar 

  • Shao Y, Wang D, (2008) Robust speaker identification using auditory features and computational auditory scene analysis. In: IEEE International conference on acoustics, speech and signal processing, IEEE, pp 1589–1592

  • Shao Y, Jin Z, Wang D, Srinivasan S, (2009) An auditory-based feature for robust speech recognition. In: IEEE international conference on acoustics, speech and signal processing, IEEE, pp 4625–4628

  • Sjöberg J, Ljung L (1995) Overtraining, regularization and searching for a minimum, with application to neural networks. Int J Control 62(6):1391–1407

    Article  Google Scholar 

  • Thakor NV, Sherman DL (2013) Eeg signal processing: Theory and applications. Neural Engineering. Springer, Berlin, pp 259–303

    Chapter  Google Scholar 

  • Valero X, Alias F (2012) Gammatone cepstral coefficients: biologically inspired features for non-speech audio classification. IEEE Trans Multimed 14(6):1684–1689

    Article  Google Scholar 

  • Weng W, Khorasani K (1996) An adaptive structure neural networks with application to eeg automatic seizure detection. Neural Networks 9(7):1223–1240

    Article  Google Scholar 

  • Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767–791

    Article  Google Scholar 

  • Yuen CT, San San W, Seong TC, Rizon M (2009) Classification of human emotions from eeg signals using statistical features and neural network. Int J Integ Eng 1(3)

Download references

Acknowledgements

We are extremely thankful to Science and Engineering Research Board (SERB), DST, Govt. of India to support this research work. The Kinect v2.0 sensors and the EEG headset used in our research experiment are purchased from the project funded by SERB with FILE NO: ECR/2017/000408. We would also like to extend our sincere thanks to the students of Department of Computer Science and Engineering, NIT Rourkela for their uninterrupted cooperation and participation catering to the data collection.

Funding

This study was funded by SERB, DST (Department of Science and Technology, Govt. Of India) for the Project file no. ECR/2017/000408.

Author information

Authors and Affiliations

  1. Machine Intelligence and Bio-Motion Research Lab, Department of Computer Science and Engineering, NIT Rourkela, Rourkela, Odisha, India

    Sumit Hazra, Acharya Aditya Pratap, Oshin Agrawal & Anup Nandy

Authors
  1. Sumit Hazra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Acharya Aditya Pratap
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oshin Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anup Nandy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sumit Hazra.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

Informed consent was obtained from all individual participants (human subjects) included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hazra, S., Pratap, A.A., Agrawal, O. et al. On effective cognitive state classification using novel feature extraction strategies. Cogn Neurodyn 15, 1125–1155 (2021). https://doi.org/10.1007/s11571-021-09688-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11571-021-09688-9

Keywords

Search

Navigation