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Conference paper(2025)
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B. Zhou, P.S.V. Sun, J. Yik, K. Van den Berghe, C. Frenkel, V. J. Reddi, A. Basu
Brain Machine Interfaces (BMI) that record signals from the motor cortex and translates these “thoughts” to action provides hope to paralyzed people. A high-accuracy decoder is needed for a seamless user experience. At the same time, it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable and was the subject of the 2024 Grand Challenge at BioCAS based on prerecorded datasets. However, in real-life, the usage of braincontrolled prosthetics, the result of decoding is presented to the user through visual feedback resulting in a closed-loop system. Hence, in the IEEE BioCAS 2025 conference, we organized the first grand challenge on Closed-Loop Neural Decoding (http://1.117.17.41/neural-decoding-grand-challenge/). The challenge requires users to move a cursor from a given start position to a target position based on spikes generated from a brain simulator. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems and the top 3 teams are invited to present their works in the IEEE BioCAS 2025.
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Brain Machine Interfaces (BMI) that record signals from the motor cortex and translates these “thoughts” to action provides hope to paralyzed people. A high-accuracy decoder is needed for a seamless user experience. At the same time, it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable and was the subject of the 2024 Grand Challenge at BioCAS based on prerecorded datasets. However, in real-life, the usage of braincontrolled prosthetics, the result of decoding is presented to the user through visual feedback resulting in a closed-loop system. Hence, in the IEEE BioCAS 2025 conference, we organized the first grand challenge on Closed-Loop Neural Decoding (http://1.117.17.41/neural-decoding-grand-challenge/). The challenge requires users to move a cursor from a given start position to a target position based on spikes generated from a brain simulator. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems and the top 3 teams are invited to present their works in the IEEE BioCAS 2025.
Conference paper(2024)
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Biyan Zhou, Pao Sheng Vincent Sun, Jason Yik, Charlotte Frenkel, Vijay Janapa Reddi, Arindam Basu
To give paralyzed people hope for a normal life, Brain Machine Interfaces (BMI) record signals from the motor cortex and a decoder translates these 'thoughts' to action. A high accuracy decoder is needed for a seamless user experience. At the same time it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable. In the IEEE BioCAS 2024 conference, we organized the first grand challenge on neural decoding for motor control. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems. There were two tracks -one preferring solutions with highest accuracy while the other gave weightage to the tradeoff between accuracy and implementation complexity. Out of the 10 teams registered for this event, the top 3 teams are invited to present their works in the IEEE BioCAS 2024.
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To give paralyzed people hope for a normal life, Brain Machine Interfaces (BMI) record signals from the motor cortex and a decoder translates these 'thoughts' to action. A high accuracy decoder is needed for a seamless user experience. At the same time it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable. In the IEEE BioCAS 2024 conference, we organized the first grand challenge on neural decoding for motor control. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems. There were two tracks -one preferring solutions with highest accuracy while the other gave weightage to the tradeoff between accuracy and implementation complexity. Out of the 10 teams registered for this event, the top 3 teams are invited to present their works in the IEEE BioCAS 2024.
