PERSON IDENTIFICATION USING FUNCTIONAL NEAR- INFRARED SPECTROSCOPY SIGNALS USING A FULLY CONNECTED DEEP NEURAL NETWORK

Year 2017, Volume: 59 Issue: 2, 55 - 68, 21.12.2017

Ozge Mercanoglu Sıncan Hacer Yalım Keles Yagmur Kır Adnan Kusman Bora Baskak

Abstract

In this study, we investigate the suitability of
functional near-infrared spectroscopy signals (fNIRS) for person identification
using data visualization and machine learning algorithms. We first applied two
linear dimension reduction algorithms: Principle Component Analysis (PCA) and
Singular Value Decomposition (SVD) in order to reduce the dimensionality of the
fNIRS data. We then inspected the clustering of samples in a 2d space using a
nonlinear projection algorithm. We observed with the SVD projection that the
data integrity associated with each person is high in the reduced space. In the
light of these observations, we implemented a random forest algorithm as a
baseline model and a fully connected deep neural network (FCDNN) as the primary
model to identify person from their brain signals. We obtained %85.16 accuracy
with our FCDNN model using SVD reduction. Our results are in parallel with the
neuroscience researches, which state that brain signals of each person are
unique and can be used to identify a person.

Keywords

functional near-infrared spectroscopy (fNIRS), person identification, PCA, SVD, fully connected deep neural network, random forest

References

• Huve, G., Takahashi, K., Hashimoto, M., “Brain activity recognition with a wearable fNIRS using neural networks”, In Mechatronics and Automation (ICMA), (2017), 1573-1578.
• Ferrari, M., Quaresima, V., “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application”, Neuroimage, 63/2 (2012), 921-935.
• Marcel, S., Millán, J. D. R., “Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation”, IEEE transactions on pattern analysis and machine intelligence, 29/4 (2007).
• Heger, D., Herff, C., Putze, F., Schultz, T., “Towards biometric person identification using fNIRS”, In Proceedings of the Fifth International Brain-Computer Interface Meeting: Defining the Future, (2013).
• Campisi, P., La Rocca, D., “Brain waves for automatic biometric-based user recognition”, IEEE transactions on information forensics and security, 9/5 (2014), 782-800.
• McDonald, D. Q., Solovey, E., “User identification from fNIRS data using deep learning”, In The First Biannual Neuroadaptive Technology Conference, (2017) 156.
• Koike, S., Nishimura, Y., Takizawa, R., Yahata, N., Kasai, K., “Near-infrared spectroscopy in schizophrenia: a possible biomarker for predicting clinical outcome and treatment response”, Frontiers in psychiatry, 4 (2013).
• Naseer, N., Hong, K. S., “fNIRS-based brain-computer interfaces: a review”, Frontiers in human neuroscience, 9 (2015).
• Hiroyasu, T., Hanawa, K., Yamamoto, U., “Gender classification of subjects from cerebral blood flow changes using deep learning”, In Computational Intelligence and Data Mining (CIDM), IEEE Symposium, (2014), 229-233.
• Boyer, M., Cummings, M. L., Spence, L. B., Solovey, E. T., “Investigating mental workload changes in a long duration supervisory control task”, Interacting with Computers, 27/5 (2015), 512-520.
• Hennrich, J., Herff, C., Heger, D., Schultz, T., “Investigating deep learning for fNIRS based BCI”, In Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE, (2015), 2844-2847.
• Hiwa, S., Hanawa, K., Tamura, R., Hachisuka, K., Hiroyasu, T., “Analyzing brain functions by subject classification of functional near-infrared spectroscopy data using convolutional neural networks analysis”, Computational intelligence and neuroscience, (2016), 3.
• Pham, T. T., Nguyen, T. D., Van Vo, T., “Sparse fNIRS feature estimation via unsupervised learning for mental workload classification”, In Advances in Neural Networks, (2016), 283-292.
• Trakoolwilaiwan, T., Behboodi, B., Choi, J. W., “Convolutional neural network for functional near-infrared spectroscopy in brain-computer interface”, (2017), 423-424.
• Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., Plumb, I., “The “Reading the Mind in the Eyes” Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism”, The Journal of Child Psychology and Psychiatry and Allied Disciplines, 42/2 (2001), 241-251.
• L.J.P. van der Maaten, Hinton, G. E., “Visualizing high-dimensional data using t-SNE”, Journal of Machine Learning Research 9 (2008), 2579-2605.
• Kinga, D., Adam, J. B., “A method for stochastic optimization”, International Conference on Learning Representations (ICLR), (2015).