TY - JOUR
T1 - Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware
AU - Behrenbeck, J.
AU - Tayeb, Z.
AU - Bhiri, C.
AU - Richter, C.
AU - Rhodes, O.
AU - Kasabov, N.
AU - Espinosa-Ramos, J.I.
AU - Furber, S.
AU - Cheng, G.
AU - Conradt, J.
PY - 2019/2/4
Y1 - 2019/2/4
N2 - Objective. The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and the realization of the spiking model on low-power neuromorphic hardware. Approach. The NeuCube spiking model was used to classify different grasp movements directly from raw surface electromyography signals (sEMG), the estimations of the applied finger forces as well as the classification of two motor imagery movements from raw electroencephalography (EEG). In a parallel investigation, the designed spiking decoder was implemented on SpiNNaker neuromorphic hardware, which allows low-energy real-time processing. Main results. Experimental results reveal a better classification accuracy using the NeuCube model compared to traditional machine learning methods. For sEMG classification, we reached a training accuracy of 85% and a test accuracy of 84.8%, as well as less than 19% of relative root mean square error (rRMSE) when estimating finger forces from six subjects. For the EEG classification, a mean accuracy of 75% was obtained when tested on raw EEG data from nine subjects from the existing 2b dataset from 'BCI competition IV'. Significance. This work provides a proof of concept for a successful implementation of the NeuCube spiking model on the SpiNNaker neuromorphic platform for raw sEMG and EEG decoding, which could chart a route ahead for a new generation of portable closed-loop and low-power neuroprostheses.
AB - Objective. The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and the realization of the spiking model on low-power neuromorphic hardware. Approach. The NeuCube spiking model was used to classify different grasp movements directly from raw surface electromyography signals (sEMG), the estimations of the applied finger forces as well as the classification of two motor imagery movements from raw electroencephalography (EEG). In a parallel investigation, the designed spiking decoder was implemented on SpiNNaker neuromorphic hardware, which allows low-energy real-time processing. Main results. Experimental results reveal a better classification accuracy using the NeuCube model compared to traditional machine learning methods. For sEMG classification, we reached a training accuracy of 85% and a test accuracy of 84.8%, as well as less than 19% of relative root mean square error (rRMSE) when estimating finger forces from six subjects. For the EEG classification, a mean accuracy of 75% was obtained when tested on raw EEG data from nine subjects from the existing 2b dataset from 'BCI competition IV'. Significance. This work provides a proof of concept for a successful implementation of the NeuCube spiking model on the SpiNNaker neuromorphic platform for raw sEMG and EEG decoding, which could chart a route ahead for a new generation of portable closed-loop and low-power neuroprostheses.
KW - NeuCube
KW - SpiNNaker
KW - spatio-temporal data
KW - EMG
KW - control of prosthetics
KW - EEG
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85062856050&partnerID=MN8TOARS
U2 - 10.1088/1741-2552/aafabc
DO - 10.1088/1741-2552/aafabc
M3 - Article
SN - 1741-2560
VL - 16
SP - 1
EP - 13
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
M1 - 026014
ER -