Classification Accuracy of an Imagined-Movements Mental Task Set for Brain-Machine Interface

2008 ◽  
Author(s):  
Michael Lochinvar S. Abundo ◽  
Eliezer A. Marco ◽  
Gino Francisco R. Mempin ◽  
Marc Caesar R. Talampas ◽  
Luis S. Sison
Keyword(s):  
Classification Accuracy ◽  
Brain Machine Interface ◽  
Mental Task ◽  
Task Set ◽  
Machine Interface

scholarly journals Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5528
Author(s):  
Peng Zhang ◽  
Lianying Chao ◽  
Yuting Chen ◽  
Xuan Ma ◽  
Weihua Wang ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
Classification Accuracy ◽  
Brain Machine Interface ◽  
Brain Machine Interfaces ◽  
Neural Data ◽  
Neural Recordings ◽  
New Knowledge ◽  
Machine Interface

Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. Methods: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. Results: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. Conclusions: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.

A prototype closed-loop brain–machine interface for the study and treatment of pain

2021 ◽  
Author(s):  
Qiaosheng Zhang ◽  
Sile Hu ◽  
Robert Talay ◽  
Zhengdong Xiao ◽  
David Rosenberg ◽  
...  
Keyword(s):  
Closed Loop ◽  
Brain Machine Interface ◽  
Machine Interface
Sign in / Sign up

Export Citation Format

Share Document