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Across neuroscience research, clinical diagnostics, and engineering applications in pain evaluation and treatment, there is a need for an objective measure of pain experience and detection when it occurs. This detector should be reliable in real-world settings using easily accessible, non-invasive data sources. We present a simple yet robust paradigm for decoding pain using neural and physiological...
Electroencephalography (EEG) processing methods mostly focus on extracting its spectral or spatial features, which are proven to discriminate bilateral hand movement, hand movement directions and speed. The focus of current study is to explore EEG time-domain features that represent neural correlates of hand movement execution speed. In this paper, we propose autocorrelation analysis of EEG and features...
This paper presents a polynomial ridge regression algorithm with substantial improvements in computational efficiency compared with the polynomial kernel ridge regression and the standard polynomial regression. This regression algorithm was combined with a Kalman Filter (KF) to yield the Directly Weighted Polynomial Ridge Regression KF (DWPRR-KF). Experiments conducted offline from data collected...
Decoding intended movement trajectory from neural activity is crucial for developing neuroprosthetic devices. In this study, we propose a processing framework to combine different information from two types of neural activities: action potentials (spikes) and local field potentials (LFPs). For this purpose, we proposed a stacked generalization approach based on recurrent neural network to enhance...
Stereo-electroencephalographic (SEEG) depth electrodes were used to record neural activity from deep brain structures in this study. By localizing all the electrodes into the individual brain, we found that areas that are inside of central sulcus occurred obvious hand-movement-related modulation when the subjects were performing different hand motion tasks. Then, an asynchronous brain-computer interface...
Current Brain Computer Interface (BCI) systems are limited by relying on neuronal spikes and decoding limited to kinematics only. For a BCI system to be practically useful, it should be able to decode brain information on a continuous basis with low latency. This study investigates if force can be decoded from local field potentials (LFP) recorded with deep brain electrodes located at the Subthalamic...
Optical techniques such as two-photon (2p) calcium imaging have the potential to transform the way we interrogate neural circuits, both in the realm of basic neuroscience and in the development of brain-machine interfaces (BMIs). This may be possible by overcoming some of the limitations of electrophysiological methods. Here we ask if optical imaging signals, in particular 2p GCaMP6 calcium imaging...
A common problem in Brain-Machine Interface (BMI) is the variations in neural signals over time, leading to significant decrease in decoding performance if the decoder is not re-trained. However, frequent re-training is not practical in real use case. In our work, we found that a temporally more robust system may be achieved through the use of wavelet transform in feature extraction. We used wavelet...
Real time algorithms for decoding physiological signals from peripheral nerve recordings form an important component of closed loop bioelectronic medicine (electroceutical) systems. As a feasibility demonstration, we considered the problem of decoding bladder pressure from pelvic nerve electroneurograms. We extracted power spectral density of the nerve signal across a band optimised for Shannon Mutual...
Linear discriminant analysis (LDA) is the most commonly used classification method for movement intention decoding from myoelectric signals. In this work, we review the performance of various discriminant analysis variants on the task of hand motion classification. We demonstrate that optimal classification performance is achieved with regularized discriminant analysis (RDA), a method which generalizes...
Brain control of prehension is thought to rely on two specific brain circuits. The dorsomedial pathway is involved in the reach movements meanwhile the dorsolateral one is dealing with grasping components. However, recent studies have demonstrated that the dorsomedial pathway also takes part in grasp movement. Raos et al. found that some neurons in dorsal premotor (PMd) show high selectivity for specific...
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