Intracortical brain-computer interface (iBCI) systems have evolved into a clinical approach to assist people with disabilities and paralysis. The decoder is one of the most crucial parts of iBCI systems. In reality, i...
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Intracortical brain-computer interface (iBCI) systems have evolved into a clinical approach to assist people with disabilities and paralysis. The decoder is one of the most crucial parts of iBCI systems. In reality, it is difficult to maintain efficient performance with a fixed decoder due to frequent changes in the distribution of data recorded over time. To improve the reliability of iBCI systems, usually, it is necessary to recalibrate decoders every time with a large amount of current data. However, it is difficult to obtain enough current data for recalibration. To reduce the recalibration data, we propose a domain adaptation-based decoder calibration framework (DA-DCF) for decoding reach-and grasp movements, which utilizes a small amount of current data and robustly achieves good performance. In the DA-DCF, a source convolutional neural network (CNN) learned representation from source data to initialize the target CNN. Secondly, the target CNN was trained through a confusing domain discriminator until it would not distinguish between the source and target domains. Thirdly, the features transformed by the target CNN were classified by the classifier for movements in the target domain. Finally, the practicality and efficiency of the DA-DCF were evaluated with other comparative frameworks. The comparative experiments were conducted on the data from a male rhesus monkey, which was trained to perform three categories of reach-and-grasp tasks across two sessions. Our results showed that DA-DCF diminished the disparity between source and target data, and significantly reduced the amount of recalibration data while improving the decoding accuracy. It is plausible to utilize DA-DCF in real-time iBCI systems of the forelimb movement control.
Objective. For nonstationarity of neural recordings, daily retraining is required in the decoder calibration of intracortical brain-machine interfaces (iBMIs). Domain adaptation (DA) has started to be applied in iBMIs...
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Objective. For nonstationarity of neural recordings, daily retraining is required in the decoder calibration of intracortical brain-machine interfaces (iBMIs). Domain adaptation (DA) has started to be applied in iBMIs to solve the problem of daily retraining by taking advantage of historical data. However, previous DA studies used only a single source domain, which might lead to performance instability. In this study, we proposed a multi-source DA algorithm, by fully utilizing the historical data, to achieve a better and more robust decoding performance while reducing the decoder calibration time. Approach. The neural signals were recorded from two rhesus macaques using intracortical electrodes to decode the reaching and grasping movements. A principal component analysis (PCA)-based multi-source domain adaptation (PMDA) algorithm was proposed to apply the feature transfer to diminish the disparities between the target domain and each source domain. Moreover, the multiple weighted sub-classifiers based on multi-source domain data and small current sample set were constructed to accomplish the decoding. Main results. Our algorithm was able to make use of the multi-source domain data and achieve better and more robust decoding performance compared with other methods. Only a small current sample set was needed by our algorithm in order for the decoder calibration time to be effectively reduced. Significance. (1) The idea of the multi-source DA was introduced into the iBMIs to solve the problem of time consumption in the daily decoder retraining. (2) Instead of using only single-source domain data in the previous study, our algorithm made use of multi-day historical data, resulting in better and more robust decoding performance. (3) Our algorithm could be accomplished with only a small current sample set, and it can effectively reduce the decoder calibration time, which is important for further clinical applications.
Objective. Intracortical brain-machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability...
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Objective. Intracortical brain-machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability, and every recalibration will require relatively large most current sample set. The aim in this study is to develop an effective decoder calibration method that can achieve good performance while minimizing recalibration time. Approach. Two rhesus macaques implanted with intracortical microelectrode arrays were trained separately on movement and sensory paradigm. Neural signals were recorded to decode reaching positions or grasping postures. A novel principal component analysis-based domain adaptation (PDA) method was proposed to recalibrate the decoder with only ultra small current sample set by taking advantage of large historical data, and the decoding performance was compared with other three calibration methods for evaluation. Main results. The PDA method closed the gap between historical and current data effectively, and made it possible to take advantage of large historical data for decoder recalibration in current data decoding. Using only ultra small current sample set (five trials of each category), the decoder calibrated using the PDA method could achieve much better and more robust performance in all sessions than using other three calibration methods in both monkeys. Significance. (1) By this study, transfer learning theory was brought into iBMIs decoder calibration for the first time. (2) Different from most transfer learning studies, the target data in this study were ultra small sample set and were transferred to the source data. (3) By taking advantage of historical data, the PDA method was demonstrated to be effective in reducing recalibration time for both movement paradigm and sensory paradigm, indicating a viable generalization. By reducing the demand for large current training data, this new method may facilitate the application o
The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces...
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The time spent in collecting current samples for decoder calibration and the computational burden brought by high-dimensional neural recordings remain two challenging problems in intracortical brain-machine interfaces (iBMIs). decoder calibration optimization approaches have been proposed, and neuron selection methods have been used to reduce computational burden. However, few methods can solve both problems simultaneously. In this article, we present a symmetrical-uncertainty-based transfer learning (SUTL) method that combines transfer learning with feature selection. The proposed method uses symmetrical uncertainty to quantitatively measure three indices for feature selection: stationarity, importance and redundancy of the feature. By selecting the stationary features, the disparities between the historical data and current data can be diminished, and the historical data can be effectively used for decoder calibration, thereby reducing the demand for current data. After selecting the important and non-redundant features, only the channels corresponding to them need to work;thus, the computational burden is reduced. The proposed method was tested on neural data recorded from two rhesus macaques to decode the reaching position or grasping gesture. The results showed that the SUTL method diminished the disparities between the historical data and current data, while achieving superior decoding performance with the needs of only ten current samples each category, less than 10% the number of features and 30% the number of neural recording channels. Additionally, unlike most studies on iBMIs, feature selection was implemented instead of neuron selection, and the average decoding accuracy achieved by the former was 6.6% higher.
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