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Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources

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Brain Informatics (BI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12241))

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Abstract

Neuronal signals allow us to understand how the brain operates and this process requires sophisticated processing of the acquired signals, which is facilitated by machine learning-based methods. However, these methods require large amount of data to first train them on the patterns present in the signals and then employ them to identify patterns from unknown signals. This data acquisition process involves expensive and complex experimental setups which are often not available to all – especially to the computational researchers who mainly deal with the development of the methods. Therefore, there is a basic need for the availability of open access datasets which can be used as benchmark towards novel methodological development and performance comparison across different methods. This would facilitate newcomers in the field to experiment and develop novel methods and achieve more robust results through data aggregation. In this scenario, this paper presents a curated list of available open access datasets of invasive neuronal signals containing a total of more than 25 datasets.

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Notes

  1. 1.

    https://bids.berkeley.edu/news/bids-megeegieeg-data-format-survey.

  2. 2.

    https://datasetsearch.research.google.com/.

References

  1. Acharya, U.R., et al.: Automated EEG analysis of epilepsy: a review. Knowl. Based Syst. 45, 147–165 (2013)

    Article  Google Scholar 

  2. Ali, H.M., Kaiser, M.S., Mahmud, M.: Application of convolutional neural network in segmenting brain regions from MRI data. In: Liang, P., Goel, V., Shan, C. (eds.) BI 2019. LNCS, vol. 11976, pp. 136–146. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37078-7_14

    Chapter  Google Scholar 

  3. Amari, S.I., et al.: Neuroinformatics: the integration of shared databases and tools towards integrative neuroscience. J. Integr. Neurosci. 1(02), 117–128 (2002)

    Article  Google Scholar 

  4. Bernert, M., Yvert, B.: An attention-based spiking neural network for unsupervised spike-sorting. Int. J. Neural Syst. 29(08), 1850059 (2019). https://doi.org/10.5281/zenodo.888977

    Article  Google Scholar 

  5. Chandravadia, N., et al.: A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task. Sci. Data 7(1), 1–12 (2020). https://doi.org/10.17605/OSF.IO/HV7JA

    Article  Google Scholar 

  6. Chao, Z.C., Nagasaka, Y., Fujii, N.: Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkey. Front. Neuroeng. (2010). http://neurotycho.org/food-tracking-task. Accessed 14 June 2020

  7. Cimbalnik, J., et al.: Physiological and pathological high frequency oscillations in focal epilepsy. Ann. Clin. Transl. Neurol. 5(9), 1062–1076 (2018). https://doi.org/10.1002/acn3.618

    Article  Google Scholar 

  8. Deweese, M.R., Zador, A.M.: Whole cell recordings from neurons in the primary auditory cortex of rat in response to pure tones of different frequency and amplitude, along with recordings of nearby local field potential (LFP) (2011). https://doi.org/10.6080/K0G44N6R

  9. Ellenrieder, N., et al.: How the human brain sleeps: direct cortical recordings of normal brain activity. Ann. Neurol. 87(2), 289–301 (2019). https://doi.org/10.1002/ana.25651

    Article  Google Scholar 

  10. Fabietti, M., et al.: Neural network-based artifact detection in local field potentials recorded from chronically implanted neural probes. In: Proceedings of IJCNN, pp. 1–8 (2020)

    Google Scholar 

  11. Ferguson, A.R., et al.: Big data from small data: data-sharing in the ‘long tail’ of neuroscience. Nat. Neurosci. 17(11), 1442–1447 (2014)

    Article  Google Scholar 

  12. Furth, K.: Replication data for: neuronal correlates of ketamine and walking induced gamma oscillations in the medial prefrontal cortex and mediodorsal thalamus (2017). https://doi.org/10.7910/DVN/MIBZLZ

  13. Hardwicke, T.E., et al.: Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal cognition. Roy. Soc. Open Sci. 5(8), 180448 (2018)

    Article  Google Scholar 

  14. Henin, S., et al.: Hippocampal gamma predicts associative memory performance as measured by acute and chronic intracranial EEG. Sci. Rep. 9(1), 1–10 (2019)

    Article  Google Scholar 

  15. KDnuggets: Amazing consistency: largest dataset analyzed/data mined - poll results and trends. https://www.kdnuggets.com/amazing-consistency-largest-dataset-analyzed-data-mined-poll-results-and-trends.html/. Accessed 14 June 2020

  16. Maguire, Y.G., et al.: Physical principles for scalable neural recording. Front. Comput. Neurosci. 7, 137 (2013)

    Google Scholar 

  17. Mahmud, M., Cecchetto, C., Vassanelli, S.: An automated method for characterization of evoked single-trial local field potentials recorded from rat barrel cortex under mechanical whisker stimulation. Cogn. Comput. 8(5), 935–945 (2016). https://doi.org/10.1007/s12559-016-9399-3

    Article  Google Scholar 

  18. Mahmud, M., Girardi, S., Maschietto, M., Vassanelli, S.: An automated method to remove artifacts induced by microstimulation in local field potentials recorded from rat somatosensory cortex. In: Proceedings of BRC, pp. 1–4 (2012). https://doi.org/10.1109/BRC.2012.6222169

  19. Mahmud, M., Girardi, S., Maschietto, M., Rahman, M.M., Bertoldo, A., Vassanelli, S.: Slow stimulus artifact removal through peak-valley detection of neuronal signals recorded from somatosensory cortex by high resolution brain-chip interface. In: Dössel, O., Schlegel, W.C. (eds.) World Congress on Medical Physics and Biomedical Engineering. IFMBE, vol. 25/4, pp. 2062–2065. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03882-2_547

    Chapter  Google Scholar 

  20. Mahmud, M., Kaiser, M.S., McGinnity, T.M., Hussain, A.: Deep learning in mining biological data. arXiv:2003.00108, pp. 1–36, February 2020

  21. Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)

    Article  MathSciNet  Google Scholar 

  22. Mahmud, M., Vassanelli, S.: Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges. Front. Neurosci. 10, 248 (2016)

    Google Scholar 

  23. Mahmud, M., Vassanelli, S.: Open-source tools for processing and analysis of in vitro extracellular neuronal signals. In: Chiappalone, M., Pasquale, V., Frega, M. (eds.) In Vitro Neuronal Networks. AN, vol. 22, pp. 233–250. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11135-9_10

    Chapter  Google Scholar 

  24. Mazzoni, A., Logothetis, N.K., Panzeri, S.: The information content of local field potentials: experiments and models (2012)

    Google Scholar 

  25. Miah, Y., Prima, C.N.E., Seema, S.J., Mahmud, M., Kaiser, M.S.: Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets. In: Proceedings of ICACIn, pp. 69–78. Springer, Singapore (2020)

    Google Scholar 

  26. Milan, J.D.R., Carmena, J.M.: Invasive or noninvasive: understanding brain-machine interface technology [conversations in BME]. IEEE Eng. Med. Biol. Mag. 29(1), 16–22 (2010)

    Article  Google Scholar 

  27. Nejedly, P.: Multicenter intracranial EEG dataset (2019). https://www.kaggle.com/nejedlypetr/multicenter-intracranial-eeg-dataset. Accessed 14 June 2020

  28. Neurotycho: neurotycho - emotional movie task (2016). http://neurotycho.org/emotional-movie-task. Accessed 14 June 2020

  29. Neurotycho: neurotycho - fixation task (2016). http://neurotycho.org/fixation-task. Accessed 14 June 2020

  30. Neurotycho: neurotycho - visual grating task (2016). http://neurotycho.org/visual-grating-task. Accessed 14 June 2020

  31. Noor, M.B.T., Zenia, N.Z., Kaiser, M.S., Mahmud, M., Al Mamun, S.: Detecting neurodegenerative disease from MRI a brief review on a deep learning perspective. In: Liang, P., Goel, V., Shan, C. (eds.) BI 2019. LNCS, vol. 11976, pp. 115–125. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37078-7_12

    Chapter  Google Scholar 

  32. Norman, Y., et al.: Data related to the article: “hippocampal sharp-wave ripples linked to visual episodic recollection in humans” (2019). https://doi.org/10.5281/ZENODO.3259369

  33. Oosugi, N., et al.: A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal. Neural Netw. 93, 1–6, September 2017. http://neurotycho.org/eeg-and-ecog-simultaneous-recording. Accessed 14 June 2020

  34. Pala, A., Petersen, C.C.: Data set for ”state-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice” (2018). https://doi.org/10.5281/ZENODO.1304771

  35. Pearson, J.M., et al.: Data from: local fields in human subthalamic nucleus track the lead-up to impulsive choices (2018). https://doi.org/10.5061/DRYAD.54TP8Q5

  36. Perrenoud, Q., Pennartz, C.M.A., Gentet, L.J.: Data from: membrane potential dynamics of spontaneous and visually evoked gamma activity in v1 of awake mice (2016). https://doi.org/10.5061/DRYAD.4754J

  37. Petersen, P.C., Hernandez, M., Buzsaki, G.: Public data repository with electrophysiological datasets collected in the Buzsaki lab (2018). https://doi.org/10.5281/ZENODO.3629881

  38. Project, E.: European epilepsy database (2007). http://epilepsy-database.eu. Accessed 14 June 2020

  39. Project, R.: Restoring Active Memory (RAM) (2018). http://memory.psych.upenn.edu/RAM. Accessed 14 June 2020

  40. Rabby, G., Azad, S., Mahmud, M., Zamli, K.Z., Rahman, M.M.: Teket: a tree-based unsupervised keyphrase extraction technique. Cogn. Comput. 12, 811–833 (2020). https://doi.org/10.1007/s12559-019-09706-3

    Article  Google Scholar 

  41. Romero, M.C., Davare, M., Armendariz, M., Janssen, P.: Neural effects of transcranial magnetic stimulation at the single-cell level. Nat. Commun. 10(1), 1–11 (2019). https://doi.org/10.5061/dryad.g54381n

    Article  Google Scholar 

  42. Schalk, G., Leuthardt, E.C.: Brain-computer interfaces using electrocorticographic signals. IEEE Rev. Biomed. Eng. 4, 140–154 (2011)

    Article  Google Scholar 

  43. Sederberg, A.J., Pala, A., Zheng, H.J.V., He, B.J., Stanley, G.B.: Data from: state-aware detection of sensory stimuli in the cortex of the awake mouse (2019). https://doi.org/10.5061/DRYAD.46CG87C

  44. Shimoda, K., Nagasaka, Y., Chao, Z.C., Fujii, N.: Decoding continuous three-dimensional hand trajectories from epidural electrocorticographic signals in Japanese macaques. J. Neural Eng. 9(3), 036015, May 2012. http://neurotycho.org/epidural-ecog-food-tracking-task. Accessed 14 June 2020

  45. Shin, H., et al.: Data from: the rate of transient beta frequency events predicts behavior across tasks and species (2017). https://doi.org/10.5061/DRYAD.PN931

  46. Teeters, J.L., et al.: Neurodata without borders: creating a common data format for neurophysiology. Neuron 88(4), 629–634 (2015)

    Article  Google Scholar 

  47. Teeters, J.L., et al.: Data sharing for computational neuroscience. Neuroinformatics 6(1), 47–55 (2008). https://doi.org/10.1007/s12021-008-9009-y

    Article  Google Scholar 

  48. Temko, A., Sarkar, A., Lightbody, G.: Detection of seizures in intracranial EEG: UPenn and mayo clinic’s seizure detection challenge. In: Proceedings of EMBC, pp. 6582–6585 (2015). https://www.kaggle.com/c/seizure-detection. Accessed 14 June 2020

  49. Theodoni, P., Rovira, B., Wang, Y., Roxin, A.: Data from: theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells (2018). https://doi.org/10.5061/DRYAD.N9C1RB0

  50. Toda, H., et al.: Simultaneous recording of ECoG and intracortical neuronal activity using a flexible multichannel electrode-mesh in visual cortex (2011). http://brainliner.jp/data/brainliner/Rat_Eye_Stimulation. Accessed 14 June 2020

  51. Vassanelli, S., Mahmud, M.: Trends and challenges in neuroengineering: toward “intelligent” neuroprostheses through brain-“brain inspired systems” communication. Front. Neurosci. 10, 438 (2016)

    Article  Google Scholar 

  52. Wagenaar, J.B., et al.: Collaborating and sharing data in epilepsy research. J. Clin. Neurophysiol. 32(3), 235 (2015)

    Article  Google Scholar 

  53. Waldert, S.: Invasive vs. non-invasive neuronal signals for brain-machine interfaces: will one prevail? Front. Neurosci. 10, 295 (2016)

    Google Scholar 

  54. Watkins, J., Fabietti, M., Mahmud, M.: Sense: a student performance quantifier using sentiment analysis. In: Proceedings of IJCNN, pp. 1–6 (2020)

    Google Scholar 

  55. Watson, B., Levenstein, D., Greene, J., Gelinas, J., Buzsáki, G.: Multi-unit spiking activity recorded from rat frontal cortex (brain regions mPFC, OFC, ACC, and M2) during wake-sleep episode wherein at least 7 minutes of wake are followed by 20 minutes of sleep. CRCNS.org (2016). https://doi.org/10.6080/K02N506Q

  56. Whittington, M., Adams, N., Hawkins, K., Hall, S.: 32 channel field array recording of V1 alpha rhythm in vitro (2020). https://doi.org/10.6084/m9.figshare.11762508.v1

  57. Wollstadt, P., et al.: Data from: breakdown of local information processing may underlie isoflurane anesthesia effects (2018). https://doi.org/10.5061/DRYAD.KK40S

  58. Yahaya, S.W., Lotfi, A., Mahmud, M.: A consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Appl. Soft Comput. 83, 105613 (2019)

    Article  Google Scholar 

  59. Zeldenrust, F., Chameau, P., Wadman, W.J.: Spike and burst coding in thalamocortical relay cells. PLoS Comput. Biol. 14(2), e1005960 (2018). https://data.donders.ru.nl/collections/di/dcn/DSC_626840_0002_144?4. Accessed 14 June 2020

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Authors and Affiliations

  1. Department of Computing and Technology, Nottingham Trent University, Clifton Lane, Clifton, Nottingham, NG11 8NS, UK

    Marcos Fabietti, Mufti Mahmud & Ahmad Lotfi

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  1. Marcos Fabietti
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  2. Mufti Mahmud
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  3. Ahmad Lotfi
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Correspondence to Mufti Mahmud .

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Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. University of Padua, Padua, Italy

    Stefano Vassanelli

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Fabietti, M., Mahmud, M., Lotfi, A. (2020). Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-59277-6_14

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