Evaluation of Spike-Detection Algorithms for a Brain-Machine Interface Application

2004 ◽  
Vol 51 (6) ◽  
pp. 905-911 ◽  
Author(s):  
I. Obeid ◽  
P.D. Wolf
Keyword(s):  
Brain Machine Interface ◽  
Spike Detection ◽  
Detection Algorithms ◽  
Machine Interface

scholarly journals An Absolute-value Detector with Threshold Comparing for Spike Detection in Brain-machine Interface

2021 ◽  
Vol 2113 (1) ◽  
pp. 012038
Author(s):  
Mingzheng Yuan
Keyword(s):  
Energy Consumption ◽  
Input Data ◽  
Minimum Energy ◽  
Full Adder ◽  
Brain Machine Interface ◽  
Optimized Design ◽  
Spike Detection ◽  
Absolute Value ◽  
Machine Interface ◽  
The Given

Abstract This research designs an absolute-value detector with the function of threshold comparing. Specifically, it is an essential device in the spike detection of the brain-machine interface. The optimized design in the research can accomplish the main functions in spike detection and has good performance in both delay and energy consumption. It comes up with two types of design at the beginning. To make the design reliable and comprehensive, it decides to discuss both methods in this paper. The first design is using a full adder, multiplexer and comparator. The concept of its logic circuit is adding the logic one to the input when the given input data is negative, keeping the original information as the given input data is positive. To achieve the function of adding, this study chooses the full adders. The primary purpose of using multiplexers is to select from the processed input and original input, and the choice depends on the most significant bit (MSB) of the input data. To compare the absolute value of the input data with a given threshold, this research used a multi-bit comparator. The second design is based on the fundamental algorithms of calculating total numbers. It indicates that this study can operate it with the threshold value through a subtractor when the input is negative. On the contrary, an adder can be used when the information is positive. Based on the concept of logic optimization, this study chooses to use the only subtractors, and it just needs to focus on the borrow bit, which can indicate the more significant number. By connecting the MSB of the input with the subtractors through XOR gates, the selection can be achieved without using any multiplexer. In the process of removing and replacing the devices, it reached the optimization of the design. Then, this paper compared the minimum delay by calculating each stage’s size and finding that the second design is better. Finally, based on the dual design, this essay computed the energy consumption in the circuit and implement VDD optimization to obtain the minimum energy.

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

A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning

2013 ◽  
Vol 461 ◽  
pp. 565-569 ◽  
Author(s):  
Fang Wang ◽  
Kai Xu ◽  
Qiao Sheng Zhang ◽  
Yi Wen Wang ◽  
Xiao Xiang Zheng
Keyword(s):  
Reinforcement Learning ◽  
Neural Activity ◽  
Neural Control ◽  
Brain Machine Interface ◽  
Learning Approach ◽  
Complex Task ◽  
Neural Data ◽  
Neural Spikes ◽  
Performance Improvements ◽  
Machine Interface

Brain-machine interfaces (BMIs) decode cortical neural spikes of paralyzed patients to control external devices for the purpose of movement restoration. Neuroplasticity induced by conducting a relatively complex task within multistep, is helpful to performance improvements of BMI system. Reinforcement learning (RL) allows the BMI system to interact with the environment to learn the task adaptively without a teacher signal, which is more appropriate to the case for paralyzed patients. In this work, we proposed to apply Q(λ)-learning to multistep goal-directed tasks using users neural activity. Neural data were recorded from M1 of a monkey manipulating a joystick in a center-out task. Compared with a supervised learning approach, significant BMI control was achieved with correct directional decoding in 84.2% and 81% of the trials from naïve states. The results demonstrate that the BMI system was able to complete a task by interacting with the environment, indicating that RL-based methods have the potential to develop more natural BMI systems.

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