Evaluation of local field potential signals in decoding of visual attention

Abstract

In the field of brain research, attention as one of the main issues in cognitive neuroscience is an important mechanism to be studied. The complicated structure of the brain cannot process all the information it receives at any moment. Attention, in fact, is considered as a possible useful mechanism in which brain concentrates on the processing of important information which is required at any certain moment. The main goal of this study is decoding the location of visual attention from local field potential signals recorded from medial temporal (MT) area of a macaque monkey. To this end, feature extraction and feature selection are applied in both the time and the frequency domains. After applying feature extraction methods such as the short time Fourier transform, continuous wavelet transform (CWT), and wavelet energy (scalogram), feature selection methods are evaluated. Feature selection methods used here are T-test, Entropy, receiver operating characteristic, and Bhattacharyya. Subsequently, different classifiers are utilized in order to decode the location of visual attention. At last, the performances of the employed classifiers are compared. The results show that the maximum information about the visual attention in area MT exists in the low frequency features. Interestingly, low frequency features over all the time-axis and all of the frequency features at the initial time interval in the spectrogram domain contain the most valuable information related to the decoding of spatial attention. In the CWT and scalogram domains, this information exists in the low frequency features at the initial time interval. Furthermore, high performances are obtained for these features in both the time and the frequency domains. Among different employed classifiers, the best achieved performance which is about 84.5 % belongs to the K-nearest neighbor classifier combined with the T-test method for feature selection in the time domain. Additionally, the best achieved result (82.9 %) is related to the spectrogram with the least number of selected features as large as 200 features using the T-test method and SVM classifier in the time−frequency domain.

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References

  1. Albright TD (1984) Direction and orientation selectivity of neurons in visual area MT of the macaque. J Neurophysiol 52(6):1106–1130

    CAS  PubMed  Google Scholar 

  2. Belitski A, Gretton A, Magri C, Murayama Y, Montemurro M, Logothetis N, Panzeri S (2008) Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information. J Neurosci 28(22):5696–5709

    CAS  Article  PubMed  Google Scholar 

  3. Belitski A, Panzeri S, Magri C, Logothetis NK, Kayser C (2010) Sensory information in local field potentials and spikes from visual and auditory cortices: time scales and frequency bands. J Comput Neurosci 29(3):533–545

    PubMed Central  Article  PubMed  Google Scholar 

  4. Bisley JW (2011) The neural basis of visual attention. J Physiol 589(1):49–57

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  5. Carrasco M (2011) Visual attention: the past 25 years. Vis Res 51:1484–1525

    PubMed Central  Article  PubMed  Google Scholar 

  6. Chen Z et al (2008) An empirical EEG analysis in brain death diagnosis for adults. Cogn Neurodyn 2(3):257–271

    PubMed Central  Article  PubMed  Google Scholar 

  7. Choi E, Lee C (2003) Feature extraction based on the Bhattacharyya distance. Pattern Recogn 36(8):1703–1709

    Article  Google Scholar 

  8. Cotic M, Chiu AWL, Jahromi SS, Carlen PL, Bardakjian BL (2011) Common time−frequency analysis of local field potential and pyramidal cell activity in seizure-like events of the rat hippocampus. J Neural Eng 8(4):046024

    CAS  Article  PubMed  Google Scholar 

  9. Coyle D, Prasad G, McGinnity TM (2005) A time−frequency approach to feature extraction for a brain–computer interface with a comparative analysis of performance measures. EURASIP J Appl Signal Process 19:3141–3151

    Article  Google Scholar 

  10. Dastidar SGh, Adeli H, Dadmehr N (2007) Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans Biomed Eng 54:1545–1551

    Article  Google Scholar 

  11. Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, London

    Google Scholar 

  12. Engel AK, Moll CKE, Fried I, Ojemann GA (2005) Invasive recordings from the human brain: clinical insights and beyond. Nat Rev Neurosci 6:35–47

    CAS  Article  PubMed  Google Scholar 

  13. Esghaei M, Daliri MR (2014) Decoding of visual attention from LFP signals of macaque MT. PLoS One 9(6):e100381

    PubMed Central  Article  PubMed  Google Scholar 

  14. Flint RD, Lindberg EW, Jordan LR, Miller LE, Slutzky MW (2012) Accurate decoding of reaching movements from field potentials in the absence of spikes. J Neural Eng 9(4):046006

  15. Gu Y, Liljenström H (2007) A neural network model of attention-modulated neurodynamics. Cogn Neurodyn 1(4):275–285

    PubMed Central  Article  PubMed  Google Scholar 

  16. Herrington TM, Assad JA (2009) Neural activity in the middle temporal area and lateral intraparietal area during endogenously cued shifts of attention. J Neurosci 29(45):14160–14176

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  17. Ince NF, Gupta R, Arica S, Tewfik AH, Ashe J, Pellizzer G (2010) High accuracy decoding of movement target direction in non-human primates based on common spatial patterns of local field potentials. PLoS One 5(12):e14384

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  18. Kajikawa Y, Schroeder ChE (2011) How local is the local field potential? Neuron 72(5):847–858

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  19. Kaliukhovich DA, Vogels R (2013) Decoding of repeated objects from local field potentials in macaque inferior temporal cortex. Plos One 8(9):e74665

  20. Katzner S, Nauhaus I, Benucci A, Bonin V, Ringach DL, Carandini M (2009) Local origin of field potentials in visual cortex. Neuron 61:35–41

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  21. Kellis S, Miller K, Thomson K, Brown R, House P, Greger B (2010) Decoding spoken words using local field potentials recorded from the cortical surface. J Neural Eng 7(5):056007

    PubMed Central  Article  PubMed  Google Scholar 

  22. Khayat PS, Niebergall R, Trujillo JCM (2010) Frequency-dependent attentional modulation of local field potential signals in macaque area MT. J Neurosci 30(20):7037–7048

    CAS  Article  PubMed  Google Scholar 

  23. Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Inform Slov 31(3):249–268

    Google Scholar 

  24. Liu J, Newsome WT (2006) Local field potential in cortical area MT: stimulus tuning and behavioral correlations. J Neurosci 26(30):7779–7790

    CAS  Article  PubMed  Google Scholar 

  25. Manyakov NV, Vogels R, Van Hulle MM (2010) Decoding stimulus–reward pairing from local field potentials recorded from monkey visual cortex. IEEE Trans Neural Netw 21(12):1892–1902

    Article  PubMed  Google Scholar 

  26. Martinez-Trujillo JC, Treue S (2004) Feature-based attention increases the selectivity of population responses in primate visual cortex. Curr Biol 14:744–751

    CAS  Article  PubMed  Google Scholar 

  27. Mehring C, Rickert J, Vaadia E, Oliveira SC, Aertsen A, Rotter S (2003) Inference of hand movements from local field potentials in monkey motor cortex. Nat Neurosci 6(12):1253–1254

    CAS  Article  PubMed  Google Scholar 

  28. Mitzdorf U (1987) Properties of the evoked potential generators: current source-density analysis of visually evoked potentials in the cat cortex. Int J Neurosci 33:33–59

    CAS  Article  PubMed  Google Scholar 

  29. Mollazadeh M, Aggarwal V, Singhal G, Law A, Davidson A, Schieber M, Thakor N (2008) Spectral modulation of LFP activity in M1 during dexterous finger movements. In: 30th Annual international IEEE EMBS conference Vancouver, Canada, pp 50314–50317

  30. Nakatani H, Orlandi N, Leeuwen CV (2011) Precisely timed oculomotor and parietal EEG activity in perceptual switching. Cogn Neurodyn 5:399–409

    PubMed Central  Article  PubMed  Google Scholar 

  31. Rasch M, Logthetis NK, Kreiman G (2009) From neurons to circuits: linear estimation of local field potentials. J Neurosci 29(44):13785–13796

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  32. Seidemann E, Newsome WT (1999) Effect of spatial attention on the responses of area MT neurons. J Neurophysiol 81:1783–1794

    CAS  PubMed  Google Scholar 

  33. Shipp S (2004) The brain circuitry of attention. Trends Cogn Sci 8(5):223–230

  34. Song Y, Huang J, Zhou D, Zha H, Giles CL (2007) IKNN: informative K-nearest neighbor pattern classification. In: 11th European conference on principles and practice of knowledge discovery in databases, Berlin, pp 248–264

  35. Starzacher A, Rinner B (2008) Evaluating KNN, LDA and QDA classification for embedded online feature fusion. In: International conference on intelligent sensors, sensor networks and information processing, Australia, pp 85–90

  36. Sundberg KA, Mitchell JF, Reynolds JH (2009) Spatial attention modulates center–surround interactions in macaque visual area V4. Neuron 61(6):952–963

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  37. Taghizadeh-Sarabi M, Daliri MR, Niksirat KS (2015) Decoding objects of basic categories from electroencephalographic signals using wavelet transform and support vector machines. Brain Topogr 28(1):33–46

  38. Theodoridis S, Koutroumbas K (2006) Pattern classification, 3rd edn. Academic Press, London

    Google Scholar 

  39. Treue S, Maunsell JHR (1999) Effects of attention on the processing of motion in macaque middle temporal and medial superior temporal visual cortical areas. J Neurosci 19(17):7591–7602

    CAS  PubMed  Google Scholar 

  40. Vecera SP, Rizzo M (2003) Spatial attention: normal processes and their breakdown. Neurol Clin 21(3):575–607

    Article  PubMed  Google Scholar 

  41. Wang Z, Logothetis NK, Liang H (2008) Decoding a bistable percept with integrated time−frequency representation of single-trial local field potential. J Neural Eng 5:433–442

    Article  PubMed  Google Scholar 

  42. Wang Z, Logothetis NK, Liang H (2009) Extraction of percept-related induced local field potential during spontaneously reversing perception. Neural Netw 22:720–727

    Article  PubMed  Google Scholar 

  43. Xu B, Song A (2008) Pattern recognition of motor imagery EEG using wavelet transform. J Biomed Sci Eng 1:64–67

    Article  Google Scholar 

  44. Zhen Z, Zeng X, Wang H, Han L (2011) A global evaluation criterion for feature selection in text categorization using Kullback–Leibler divergence. In: International conference of soft computing and pattern recognition, China, pp 440–445

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Acknowledgement

We would like to thank Prof. Stefan Treue for providing the infrastructure, intellectual and financial support for recording the data.

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Correspondence to Mohammad Reza Daliri.

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Seif, Z., Daliri, M.R. Evaluation of local field potential signals in decoding of visual attention. Cogn Neurodyn 9, 509–522 (2015). https://doi.org/10.1007/s11571-015-9336-2

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Keywords

  • Local field potential
  • Decoding of visual attention
  • Extracellular recording
  • Feature extraction and feature selection