Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Decoding Force using Signals Recorded from the Rat Motor Cortex by Linear Regression

Document Type : Full Research Paper

Authors

Tahmineh Sadati 1
Mohammad Reza Daliri 2

1 Master Student, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

2 Associate Professor, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

https://doi.org/10.22041/ijbme.2018.79524.1316
Abstract
A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ability to provide a new dimension in communication or control of a device for the disabled persons. The neural activity used in the brain-computer interface can be recorded by various invasive and non-invasive methods and can be converted to the desired signal by different decoding algorithms. In this study, 3 rats were used to perform a movement task which was pressing a key and receiving a drop of water by a mechanical arm for corrected trials. By implanting a 16-channel microelectrode array in the rat's motor cortex during an invasive process, the brain signals are recorded during the task, and simultaneously the signal received by the force sensor is also stored. By performing the necessary preprocessing on spikes and extracting the firing rates of signal as a feature vector by convolving a Gaussian window with the spike trains, the necessary inputs for the decoding algorithm, which is linear regression here, are obtained. Two patterns have been used for cross validation. The first pattern considers 60% of the data from the beginning of the signal as a train set and the remaining 40% of the signal as a test set and the second pattern is the opposite of the first one. Several methods have been used to evaluate the decoding algorithm used in the studies. In this paper, the correlation coefficient and coefficient of determination have been used. The correlation coefficient and coefficient of determination between the desired force and predicted force in linear resgression method, in average of three sessions for three rats, are equal to r=0.56 and =0.20 for the first pattern and r=0.55 and =0.30 for the second pattern respectively. These results show that firing rates of neurons are proper features to predict continous variables such as force. Besides, it can be concluded that linear regression is a suitable method for decoding a motor variable like force and follows the desired signal properly.

Keywords

Brain-Computer Interface
Decoding
Movement Task
Spike Trains
linear regression
Correlation Coefficient
Coefficient of Determination

Subjects

[1]     Mano, M., Capi, G., Tanaka, N., & Kawahara, S. (2013). An artificial neural network based robot controller that uses rat’s brain signals. Robotics, 2(2), 54-65.

[2]     Vallabhaneni, A., Wang, T., & He, B. (2005). Brain—computer interface. Neural engineering, 85-121.
[3]     Foodeh, R., Khorasani, A., Shalchyan, V., & Daliri, M. R. (2017). Minimum Noise Estimate Filter: A Novel Automated Artifacts Removal Method for Field Potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(8), 1143-1152.
[4]     Lungu, I. A., Riehle, A., Nawrot, M. P., & Schmuker, M. (2017). Predicting voluntary movements from motor cortical activity with neuromorphic hardware. IBM Journal of Research and Development, 61(2/3), 5-1.
[5]     Chapin, J. K., Moxon, K. A., Markowitz, R. S., & Nicolelis, M. A. (1999). Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nature neuroscience, 2(7), 664-670.
[6]     Olson, B. P., Si, J., Hu, J., & He, J. (2005). Closed-loop cortical control of direction using support vector machines. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13(1), 72-80.
[7]     Francis, J. T., & Chapin, J. K. (2006). Neural ensemble activity from multiple brain regions predicts kinematic and dynamic variables in a multiple force field reaching task. IEEE Transactions on neural systems and rehabilitation engineering, 14(2), 172-174.
[8]     Khorasani, A., Beni, N. H., Shalchyan, V., & Daliri, M. R. (2016). Continuous Force Decoding from Local Field Potentials of the Primary Motor Cortex in Freely Moving Rats. Scientific reports, 6, 35238.
[9]     Liu, Y., Coon, W. G., de Pesters, A., Brunner, P., & Schalk, G. (2015). The effects of spatial filtering and artifacts on electrocorticographic signals. Journal of neural engineering, 12(5), 056008.
[10] Rey, H. G., Pedreira, C., & Quiroga, R. Q. (2015). Past, present and future of spike sorting techniques. Brain research bulletin, 119, 106-117.
[11] Song, W., Ramakrishnan, A., Udoekwere, U. I., & Giszter, S. F. (2009). Multiple types of movement-related information encoded in hindlimb/trunk cortex in rats and potentially available for brain–machine interface controls. IEEE Transactions on Biomedical Engineering, 56(11), 2712-2716.
[12] ‌‌Spüler, M., Sarasola-Sanz, A., Birbaumer, N., Rosenstiel, W., & Ramos-Murguialday, A. (2015, August). Comparing metrics to evaluate performance of regression methods for decoding of neural signals. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 1083-1086). IEEE.
Iranian Journal of Biomedical Engineering (IJBME)
Volume 12, Issue 1
Spring 2018
Pages 1-10

  • Receive Date 16 January 2018
  • Revise Date 24 June 2018
  • Accept Date 17 July 2018

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