NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics’ Control
Abstract
1. Introduction
- The task for neural response decoding (NRD) for the aim of MCD improvement is presented as a reinforcement learning framework.
- The MCD (actor) and NRD (critic) are designed as neuromorphic structures combining a 3D-SNN structure for spatio-temporal feature extraction from ECoG implants and an on-line trainable ESN for the final decoding stage, which makes their implementation suitable for a neuromorphic chip, offering low power consumption, fast processing and small size.
- Several approaches to training both actor (MCD) and critic (NRD) in their interaction over time are investigated.
- Potential for on-line MCD improvement using NRD predictions is proven via simulations.
2. Materials and Methods
2.1. Experimental Data
2.2. Neuromorphic Framework for MCD and NRD
- A filtering module that transforms the raw ECoG signals to input signals for 3D-SNN using Morlet wavelet transformation for multiple central frequencies and their combination into a feature matrix of the same size as the original one.
- A 3D recurrent SNN architecture called a 3D SNN cube, which is spatially structured and adaptable to an individual 3D brain template, is used for feature extraction from processed ECoG signals. It adapts continuously to the incoming input in unsupervised mode via the STDP rule.
- Two recurrent Echo state network (ESN) structures for decoding of the desired movement (MCD) and satisfaction (NRD) from extracted features (spiking frequencies of the selected neurons in the 3D-SNN module). It can be trained on-line in supervised mode via recursive least squares (RLS) or in an unsupervised regime via reinforcement learning (RL) rules.
2.3. Software Implementation
3. Methodology
3.1. Training Approaches
- is denoted henceforth as TA1 (Figure 5): Use the desired state of the MCD from the DB denoted as as a target for the MCD and input to the NRD no matter whether the training example is labeled as satisfactory or non-satisfactory.
- is denoted henceforth as TA2 (Figure 6): Use the swapped desired state of the MCD denoted as (if , revert to , and vise versa) as a target for the MCD and input for the NRD if the training example is labeled as non-satisfactory in the DB ().
- : Use the (TA1) to train the initial models of both the MCD and NRD using only the first training session from the DB.
Algorithm 1 Pseudo-code of training algorithm |
Initialization Initialize and module parameters Compose 3D-SNN module using ECoG positions Initialize the cube connection weights based on the neurons’ distances while do
|
3.2. Testing Experiments
Algorithm 2 Pseudo-code of testing algorithm |
Initialization Set and module parameters to the trained ones Compose 3D-SNN module using ECoG positions Set 3D-SNN state to the achieved after training Set cube connection weights to the values achieved after training while do
|
- is denoted further as TE1 (Figure 9): Feed the trained NRD with the desired action from the DB () rather than from the trained MCD prediction. In this way, we skip the MCD imitating knowledge about instructions on the screen. However, in on-line mode, the NRD must know the target action, which is not always possible.
- is denoted further as TE2 (Figure 10): Feed the trained MCD prediction () to the NRD, which is not always correct but will be available in a real situation. In this way, the decoder works fully in on-line mode.
- is denoted further as TE3: Testing of both models trained via the third (TA3) and fourth (TA4) training approaches was carried out as in the second experiment TE2, i.e., in on-line mode.
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Experiment | Reinforcement Learning |
---|---|
Patient | Object |
ECoG features | |
MCD | Actor |
State | Action |
NRD | Critic |
Satisfaction | Reinforcement signal |
Training Approach | Metrics | Session 8 | Session 9 |
---|---|---|---|
TA1 | Balanced Accuracy | 0.7491 | 0.7616 |
TA2 | Balanced Accuracy | 0.6474 | 0.5456 |
TA1 | on | 0.4145 | 0.4924 |
TA2 | on | 0.3643 | 0.1546 |
TA1 | on | 0.9219 | 0.9610 |
TA2 | on | 0.9491 | 0.9586 |
Training Approach | Metrics | Session 8 | Session 9 |
---|---|---|---|
TA1 | Balanced Accuracy | 0.6279 | 0.5578 |
TA2 | Balanced Accuracy | 0.5291 | 0.4967 |
TA1 | on | 0.2796 | 0.1549 |
TA2 | on | 0.1386 | 0.0627 |
TA1 | on | 0.9177 | 0.9162 |
TA2 | on | 0.9028 | 0.9304 |
Training Approach | Metrics | Session 8 | Session 9 |
---|---|---|---|
TA3 | Balanced Accuracy | 0.5637 | 0.5501 |
TA4 | Balanced Accuracy | 0.6787 | 0.6424 |
TA3 | on | 0.1800 | 0.1391 |
TA4 | on | 0.2761 | 0.2350 |
TA3 | on | 0.8657 | 0.8793 |
TA4 | on | 0.8532 | 0.9028 |
Training Approach | NRD Feedback | Metrics | Session 8 | Session 9 |
---|---|---|---|---|
TA1 | YES | Balanced Accuracy | 0.8069 | 0.7593 |
TA1 | NO | Balanced Accuracy | 0.8370 | 0.7699 |
TA2 | YES | Balanced Accuracy | 0.8723 | 0.8715 |
TA2 | NO | Balanced Accuracy | 0.8304 | 0.8251 |
TA1 | YES | on | 0.7861 | 0.7272 |
TA1 | NO | on | 0.8221 | 0.7393 |
TA2 | YES | on | 0.8389 | 0.8589 |
TA2 | NO | on | 0.8154 | 0.8086 |
TA1 | YES | on | 0.8285 | 0.7879 |
TA1 | NO | on | 0.8490 | 0.7973 |
TA2 | YES | on | 0.8767 | 0.8886 |
TA2 | NO | on | 0.8416 | 0.8415 |
Training Approach | NRD Feedback | Metrics | Session 8 | Session 9 |
---|---|---|---|---|
TA3 | YES | Balanced Accuracy | 0.7942 | 0.7691 |
TA3 | NO | Balanced Accuracy | 0.8370 | 0.7699 |
TA4 | YES | Balanced Accuracy | 0.8724 | 0.8800 |
TA4 | NO | Balanced Accuracy | 0.8366 | 0.8403 |
TA3 | YES | on | 0.7528 | 0.7363 |
TA3 | NO | on | 0.8221 | 0.7393 |
TA4 | YES | on | 0.8400 | 0.8693 |
TA4 | NO | on | 0.8209 | 0.8273 |
TA3 | YES | on | 0.8130 | 0.8047 |
TA3 | NO | on | 0.8490 | 0.7973 |
TA4 | YES | on | 0.8812 | 0.8945 |
TA4 | NO | on | 0.8508 | 0.8536 |
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Rusev, G.; Yordanov, S.; Nedelcheva, S.; Banderov, A.; Lafaye de Micheaux, H.; Sauter-Starace, F.; Aksenova, T.; Koprinkova-Hristova, P.; Kasabov, N. NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics’ Control. Biomimetics 2025, 10, 518. https://doi.org/10.3390/biomimetics10080518
Rusev G, Yordanov S, Nedelcheva S, Banderov A, Lafaye de Micheaux H, Sauter-Starace F, Aksenova T, Koprinkova-Hristova P, Kasabov N. NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics’ Control. Biomimetics. 2025; 10(8):518. https://doi.org/10.3390/biomimetics10080518
Chicago/Turabian StyleRusev, Georgi, Svetlozar Yordanov, Simona Nedelcheva, Alexander Banderov, Hugo Lafaye de Micheaux, Fabien Sauter-Starace, Tetiana Aksenova, Petia Koprinkova-Hristova, and Nikola Kasabov. 2025. "NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics’ Control" Biomimetics 10, no. 8: 518. https://doi.org/10.3390/biomimetics10080518
APA StyleRusev, G., Yordanov, S., Nedelcheva, S., Banderov, A., Lafaye de Micheaux, H., Sauter-Starace, F., Aksenova, T., Koprinkova-Hristova, P., & Kasabov, N. (2025). NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics’ Control. Biomimetics, 10(8), 518. https://doi.org/10.3390/biomimetics10080518