TY - GEN
T1 - Cortical motor intention decoding on an analog co-processor with fast training for non-stationary data
AU - Shaikh, Shoeb
AU - Yi, Chen
AU - Basu, Arindam
AU - So, Rosa
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2018/3/23
Y1 - 2018/3/23
N2 - This paper presents a low power hardware implementation of a motor intention decoder used in intra-cortical Brain Machine Interfaces. It offers two specific advantages over current state of the art decoders. Firstly, the decoding is done on an analog co-processor instead of a personal computer thereby reducing both the power consumption and size of the overall system. Secondly, the co-processor employs a randomized neural network - extreme learning machine (ELM), which is as quick to train as the linear decoders while being adept at capturing the complex non-linear mappings between the neural activity and the intended movements. Results show an average 10% improvement in decoding accuracy over linear discriminant analysis in non-stationary datasets.
AB - This paper presents a low power hardware implementation of a motor intention decoder used in intra-cortical Brain Machine Interfaces. It offers two specific advantages over current state of the art decoders. Firstly, the decoding is done on an analog co-processor instead of a personal computer thereby reducing both the power consumption and size of the overall system. Secondly, the co-processor employs a randomized neural network - extreme learning machine (ELM), which is as quick to train as the linear decoders while being adept at capturing the complex non-linear mappings between the neural activity and the intended movements. Results show an average 10% improvement in decoding accuracy over linear discriminant analysis in non-stationary datasets.
KW - Brain-machine interfaces
KW - low power hardware
KW - machine learning
KW - motor intention decoding
KW - neural network
UR - https://www.scopus.com/pages/publications/85049961435
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85049961435&origin=recordpage
U2 - 10.1109/BIOCAS.2017.8325073
DO - 10.1109/BIOCAS.2017.8325073
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509058037
VL - 2018-January
T3 - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
SP - 1
EP - 4
BT - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings
PB - IEEE
T2 - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017
Y2 - 19 October 2017 through 21 October 2017
ER -