TY - GEN
T1 - Decoding Kinematics from Human Parietal Cortex using Neural Networks
AU - Shah, Sahil
AU - Haghi, Benyamin
AU - Kellis, Spencer
AU - Bashford, Luke
AU - Kramer, Daniel
AU - Lee, Brian
AU - Liu, Charles
AU - Andersen, Richard
AU - Emami, Azita
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/16
Y1 - 2019/5/16
N2 - Brain-machine interfaces have shown promising results in providing control over assistive devices for paralyzed patients. In this work we describe a BMI system using electrodes implanted in the parietal lobe of a tetraplegic subject. Neural data used for the decoding was recorded in five 3-minute blocks during the same session. Within each block, the subject uses motor imagery to control a cursor in a 2D center-out task. We compare performance for four different algorithms: Kalman filter, a two-layer Deep Neural Network (DNN), a Recurrent Neural Network (RNN) with SimpleRNN unit cell (SimpleRNN), and a RNN with Long-Short-Term Memory (LSTM) unit cell. The decoders achieved Pearson Correlation Coefficients (ρ) of 0.48, 0.39, 0.77 and 0.75, respectively, in the Y-coordinate, and 0.24, 0.20, 0.46 and 0.47, respectively, in the X-coordinate.
AB - Brain-machine interfaces have shown promising results in providing control over assistive devices for paralyzed patients. In this work we describe a BMI system using electrodes implanted in the parietal lobe of a tetraplegic subject. Neural data used for the decoding was recorded in five 3-minute blocks during the same session. Within each block, the subject uses motor imagery to control a cursor in a 2D center-out task. We compare performance for four different algorithms: Kalman filter, a two-layer Deep Neural Network (DNN), a Recurrent Neural Network (RNN) with SimpleRNN unit cell (SimpleRNN), and a RNN with Long-Short-Term Memory (LSTM) unit cell. The decoders achieved Pearson Correlation Coefficients (ρ) of 0.48, 0.39, 0.77 and 0.75, respectively, in the Y-coordinate, and 0.24, 0.20, 0.46 and 0.47, respectively, in the X-coordinate.
UR - https://www.scopus.com/pages/publications/85066743671
UR - https://www.scopus.com/inward/citedby.url?scp=85066743671&partnerID=8YFLogxK
U2 - 10.1109/NER.2019.8717137
DO - 10.1109/NER.2019.8717137
M3 - Conference contribution
AN - SCOPUS:85066743671
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 1138
EP - 1141
BT - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Y2 - 20 March 2019 through 23 March 2019
ER -