Time-Varying Spectral Analysis of Single-Channel EEG: Application in Affective Protocol

  • F. SircaEmail author
  • F. Onorati
  • L. Mainardi
  • V. Russo
Original Article


Neural correlates of emotions have been widely investigated using noninvasive sensor modalities. These approaches are often characterized by a low level of usability and are not practical for real-life situations. The aim of this study is to show that a single electroencephalography (EEG) electrode placed in the central region of the scalp is able to discriminate emotionally characterized events with respect to a baseline period. Emotional changes were induced using an imagery approach based on the recall of autobiographical events characterized by four basic emotions: “Happiness”, “Fear”, “Anger”, and “Sadness”. Data from 17 normal subjects were recorded at the Cz position according to the International 10–20 system. After preprocessing and artifact detection phases, raw signals were analyzed through a time-variant adaptive autoregressive model to extract EEG characteristic spectral components. Five frequency bands, i.e., the classical EEG rhythms, were considered, namely the delta band (δ) (1–4 Hz), the theta band (θ) (4–6 Hz), the alpha band (α) (6–12 Hz), the beta band (β) (12–30 Hz), and the gamma band (γ) (30–50 Hz). The relative powers of the EEG rhythms were used as features to compare the experimental conditions. Our results show statistically significant differences when comparing the power content in the gamma band of baseline events versus emotionally characterized events. Particularly, a significant increase in gamma band relative power was found in 3 out of 4 emotionally characterized events (“Happiness”, “Sadness”, and “Anger”). In agreement with previous studies, our findings confirm the presence of a possible correlation between broader high-frequency cortical activation and affective processing of the brain. The present study shows that a single EEG electrode could potentially be used for the assessment of the emotional state with a minimally invasive setup.


Electroencephalography (EEG) Emotions Single-channel EEG Adaptive autoregressive model (AAR) Affective protocol 


  1. 1.
    Mikhail, M., & El-Ayat, K. (2013). Using minimal number of electrodes for emotion detection using brain signals produced from a new elicitation technique. International Journal of Autonomous and Adaptive Communications Systems, 6, 80–97.CrossRefGoogle Scholar
  2. 2.
    Mauss, I. B., & Robinson, M. D. (2009). Measures of emotion: A review. Cognition and Emotion, 23, 209–237.CrossRefzbMATHGoogle Scholar
  3. 3.
    Panksepp, J. (2007). Neuro-psychoanalysis may enliven the mindbrain sciences. Cortex, 43, 1106–1107.CrossRefGoogle Scholar
  4. 4.
    Kim, M.-K., Kim, M., Oh, E., & Kim, S.-P. (2013). A review on the computational methods for emotional state estimation from the human EEG. Computational and Mathematical Methods in Medicine, 2013, 579–734.MathSciNetGoogle Scholar
  5. 5.
    Vytal, K., & Hamann, S. (2010). Neuroimaging support for discrete neural correlates of basic emotions: A voxel-based metaanalysis. Journal of Cognitive Neuroscience, 22, 2864–2885.CrossRefGoogle Scholar
  6. 6.
    Peyk, P., Schupp, H. T., Elbert, T., & Junghöfer, M. (2008). Emotion processing in the visual brain: A MEG analysis. Brain Topography, 20, 205–215.CrossRefGoogle Scholar
  7. 7.
    Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993). Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65, 413–497.CrossRefGoogle Scholar
  8. 8.
    Ray, A., & Bowyer, S. M. (2010). Clinical applications of magnetoencephalography in epilepsy. Annals of Indian Academy of Neurology, 13, 14–22.CrossRefGoogle Scholar
  9. 9.
    Balconi, M., & Mazza, G. (2009). Brain oscillations and BIS/BAS (behavioral inhibition/activation system) effects on processing masked emotional cues. ERS/ERD and coherence measures of alpha band. International Journal of Psychophysiology, 74, 158–165.CrossRefGoogle Scholar
  10. 10.
    Li, M., & Lu, B. L. (2009). Emotion classification based on gamma band EEG. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, (pp. 12231226).Google Scholar
  11. 11.
    Müller, M. M., Keil, A., Gruber, T., & Elbert, T. (1999). Processing of affective pictures modulates right-hemispheric gamma band EEG activity. Clinical Neurophysiology, 110, 1913–1920.CrossRefGoogle Scholar
  12. 12.
    Keil, A., Müller, M. M., Gruber, T., Wienbruch, C., Stolarova, M., & Elbert, T. (2001). Effects of emotional arousal in the cerebral hemispheres: a study of oscillatory brain activity and event related potentials. Clinical Neurophysiology, 112, 2057–2068.CrossRefGoogle Scholar
  13. 13.
    Murugappan, M., Nagarajan, R., & Yaacob, S. (2011). Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals. Journal of Medical and Biological Engineering, 31, 45–51.CrossRefGoogle Scholar
  14. 14.
    Hosseini, S. A., & Naghibi-Sistani, M. B. (2011). Emotion recognition method using entropy analysis of EEG signals. International Journal of Image, Graphics and Signal Processing (IJIGSP), 3, 30–36.CrossRefzbMATHGoogle Scholar
  15. 15.
    Jiea, X., Rui, C., & Li, L. (2014). Emotion recognition based on the sample entropy of EEG. Bio-Medical Materials and Engineering, 24, 1185–1192.Google Scholar
  16. 16.
    Min, Y. K., Chung, S. C., & Min, B. C. (2005). Physiological evaluation on emotional change induced by imagination. Applied Psychophysiology and Biofeedback, 30, 137–150.CrossRefGoogle Scholar
  17. 17.
    Onorati, F., Barbieri, R., Mauri, M., Russo, V., & Mainardi, L. (2013). Characterization of affective states by pupillary dynamics and autonomic correlates. Frontiers in Neuroengineering, 6, 9.CrossRefGoogle Scholar
  18. 18.
    Mauri, M., Onorati, F., Russo, V., Mainardi, L., & Barbieri, R. (2012). Psycho-physiological assessment of emotions. International Journal of Bioelectromagnetism, 14, 133–140.zbMATHGoogle Scholar
  19. 19.
    Croft, R. J., & Barry, R. J. (2000). Removal of ocular artifact from the EEG: A review. Clinical Neurophysiology, 30, 5–19.CrossRefGoogle Scholar
  20. 20.
    Joyce, C. A., Gorodnitsky, I. F., & Kutas, M. (2004). Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology, 41, 313–325.CrossRefGoogle Scholar
  21. 21.
    Florian, G., & Pfurtscheller, G. (1995). Dynamic spectral analysis of event-related EEG data. Electroencephalography and Clinical Neurophysiology, 95, 393–396.CrossRefGoogle Scholar
  22. 22.
    Bianchi, A. M. (2011). Time-variant spectral estimation. In S. Cerutti & C. Marchesi (Eds.), Advanced Methods of Biomedical Signal Processing (pp. 259–288). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  23. 23.
    Bittanti, S., & Campi, M. (1994). Bounded error identification of time-varying parameters by RLS techniques. IEEE Transactions on Automatic Control, 39, 1106–1110.MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Baselli, G., Cerutti, S., Civardi, S., Lombardi, F., Malliani, M., Merri, M., et al. (1987). Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies. International Journal of Bio-Medical Computing, 20, 51–70.CrossRefGoogle Scholar
  25. 25.
    Zetterberg, L. H. (1969). Estimation of parameters for a linear difference equation with application to EEG analysis. Mathematical Biosciences, 5, 227–275.MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Lilliefors, H. (1967). On the kolmogorov–smirnov test for normality with mean and variance unknown. Journal of American Statistical Association, 62, 399–402.CrossRefzbMATHGoogle Scholar
  27. 27.
    Levene, H. (1960). Robust testes for equality of variances. In I. Olkin (Ed.), Contributions to probability and statistics (pp. 278–292). Palo Alto CA: Stanford University Press.Google Scholar
  28. 28.
    Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010). Emotion recognition from EEG using higher order crossings. IEEE Transactions on Information Technology in Biomedicine, 14, 186–197.CrossRefGoogle Scholar
  29. 29.
    Aftanas, L. I., Varlamov, A. A., Pavlov, S. V., Makhnev, V. P., & Reva, N. V. (2001). Affective picture processing: event-related synchronization within individually defined human theta band is modulated by valence dimension. Neuroscience Letters, 303, 115–118.CrossRefGoogle Scholar

Copyright information

© European Union 2015

Authors and Affiliations

  1. 1.Behavior & Brain LabIULM UniversityMilanItaly
  2. 2.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico of MilanMilanItaly

Personalised recommendations