| Summary: | 碩士 === 國立陽明大學 === 生物醫學工程學系 === 105 === Neuroscience research has been paid more and more attention in recent years, and one of the research is the brain machine interfaces (BMIs). In BMIs, in addition to solve the curse of dimensionality, the accuracy and stability of the decoding algorithm is also important. The architecture of the signal acquisition in BMIs could be divided into invasive and non-invasive. For the invasive BMIs, researches are based on intracranial EEG (iEEG), electrocorticography (ECoG), intracortical local field potentials (LFPs), or neuronal spiking activity (AP). Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithm (PVA), optimal linear estimator (OLE), principal component analysis (PCA), partial least squares (PLS), Wiener filter (WF), Kalman filter (KF), Bayesian filter (BF), neural network (NN), and so on. Furthermore, depending on the type of the input, there are spike-driven and LFP-driven decoders. Both of them have high accuracy, and therefore are frequently used.
This study adopts kernel sliced inverse regression (kSIR) to predict intended forelimb movement trajectories according to the recorded neurons from primary motor (M1) cortex. kSIR is a novel decoding algorithm, and is useful even when signals obtained from a smaller number of neurons. The LFPs was adopted to improve the performance, and hoped to improve the accuracy and stability. Results showed that the stability and accuracy were significantly improved, where the accuracy (R squared, Mean ± SEM) was improved from ("0.88"±"0.059" ) to ("0.93"±"0.061)" for x-axis and ("0.90"±"0.022)" to ("0.97"±"0.024)" for y-axis. And the adjustment of kernel bandwidth is necessary due to the deviation of observed distribution from Gaussian. Therefore, an adaptive architecture was applied to adjust the parameter using in kSIR. The superiority of this multi-input kSIR was obviously. To sum up, simultaneously using both spikes and LFPs would make the decoder more robust and accurate, even when met conditions with sparse neural information.
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