Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain–machine interface

Jun, Dai; Zhang, Peng; Sun, Honghao; Qiao, Xueguang; Zhao, Yuwei; Ma, Jinxu; Li, Shaohua; Zhou, Jin; Wang, Changyong

doi:10.1088/1741-2552/ab0bfb

Cited by 28 publications

(13 citation statements)

References 47 publications

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“…LDA and SVM have been quite popularly employed in both invasive and non-invasive forms of brain machine interfaces [44]- [46]. ELM has been shown to exhibit comparable results to the state of the art algorithms such as SVM, along with the benefit of being trained quickly on a small amount of training data [47], [48].…”

Section: Resultsmentioning

confidence: 99%

Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs

Shaikh

Sibindi

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels. We have tested the generality of this technique on different base classifiers -linear discriminant analysis (LDA), support vector machine (SVM), extreme learning machine (ELM) and multilayer perceptron (MLP). Results show decoding accuracy improvements of up to ≈ 21%, 13%, 19%, 10% in non-human primate (NHP) A and 7%, 9%, 7%, 9% in NHP B across test days while using the sparse ensemble approach over a single classifier model for LDA, SVM, ELM and MLP algorithms respectively. The technique also holds ground when the most informative electrode on the test day is dropped. Accordingly, improvements of up to ≈ 24%, 11%, 22%, 9% in NHP A and 14%, 19%, 7%, 28% in NHP B are obtained for LDA, SVM, ELM and MLP respectively.

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Section: Resultsmentioning

confidence: 99%

Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs

Shaikh

Sibindi

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The wideband signals were band-pass filtered between 250 Hz and 6 kHz. Threshold crossing [ 44 ] was used to detect the spike, and the threshold was set as a value of –4.5 times the root mean square of the signal in each channel. All channels of the arrays in monkey M worked well, whereas only 66 channels of the arrays in S1 and PPC could record spike signals for monkey B.…”

Section: Methodsmentioning

confidence: 99%

Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

Zhang

Chao

Chen

et al. 2020

Sensors

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Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. Methods: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. Results: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. Conclusions: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.

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“…The sampling rate was 40 kHz, and the neural signals were bandpass filtered between 250 Hz and 6 kHz. Spike counts were detected using the threshold crossing method, and the threshold was set to -4.5 times the root mean square value of the spike band in each channel [44], [45]. The threshold crossing method is a standard practice in iBMIs.…”

Section: A Experimental Set-up and Data Collectionmentioning

confidence: 99%

Feature-Selection-Based Transfer Learning for Intracortical Brain–Machine Interface Decoding

Zhang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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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 paper, we present a symmetrical-uncertaintybased 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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Reliability of motor and sensory neural decoding by threshold crossings for intracortical brain–machine interface

Cited by 28 publications

References 47 publications

Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs

Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs

Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface

Feature-Selection-Based Transfer Learning for Intracortical Brain–Machine Interface Decoding

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