Volume 18, Issue 2 (Journal of Control, V.18, N.2 Summer 2024)
JoC 2024, 18(2): 25-36 |
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Katoozian D, Hosseini-Nejad H, Abolghasemi M. A Neural Decoding Method Based on Neurons Activities Pattern Recognition for Implantable Intra-cortical BMIs. JoC 2024; 18 (2) :25-36
URL: http://joc.kntu.ac.ir/article-1-1015-en.html
URL: http://joc.kntu.ac.ir/article-1-1015-en.html
1- Faculty of Electrical Engineering of K.N. Toosi University of Technology
2- Faculty of Electrical and computer of Tehran Uinversity
2- Faculty of Electrical and computer of Tehran Uinversity
Abstract: (2280 Views)
Converting motor intention to a machine command is called decoding in Brain Machine Interface (BMI) field. Despite recent advances, decoding remains among the most challenging steps in BMI. Furthermore, the majority of algorithms currently used in decoding require a computer, as a result of their high computational complexity. However, due to the size and power consumption of computers, they are not practical for implantable BMI systems. To address this issue, this paper proposes a novel approach based on hyperdimensional computing. This approach involves the conversion of the input space to binary, followed by the selection of the most similar vector to the answer. The proposed method is evaluated using a real dataset recorded from the Frontal Eye Field (FEF) of two male rhesus monkeys, with eight possible angles as the output space. The results demonstrate an accuracy rate of 51.5% with very low computational complexity. Furthermore, the proposed algorithm is implemented on a field-programmable gate array, indicating that it is a practical choice for real-time implantable BMI applications requiring a low computational cost method with a medium level of accuracy.
Keywords: Intra-cortical neural decoding, Neurons activities patterns, Hyperdimensional computing, Hardware implementation
Type of Article: Research paper |
Subject:
Special
Received: 2023/12/10 | Accepted: 2024/03/13 | ePublished ahead of print: 2024/05/4 | Published: 2024/09/20
Received: 2023/12/10 | Accepted: 2024/03/13 | ePublished ahead of print: 2024/05/4 | Published: 2024/09/20
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