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Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications, we developed a real-time system that uses(More)
We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a(More)
The direct neural control of external devices such as computer displays or prosthetic limbs requires the accurate decoding of neural activity representing continuous movement. We develop a real-time control system using the spiking activity of approximately 40 neurons recorded with an electrode array implanted in the arm area of primary motor cortex. In(More)
Statistical learning and probabilistic inference techniques are used to infer the hand position of a subject from multi-electrode recordings of neu-ral activity in motor cortex. First, an array of electrodes provides training data of neural firing conditioned on hand kinematics. We learn a non-parametric representation of this firing activity using a(More)
Partly motivated by entropy-estimation problems in neuroscience, we present a detailed and extensive comparison between some of the most popular and effective entropy estimation methods used in practice: The plug-in method, four different es-timators based on the Lempel-Ziv (LZ) family of data compression algorithms, an estimator based on the Context-Tree(More)
Magnetic resonance spectroscopic imaging requires a great deal of time to gather the data necessary to achieve satisfactory resolution. When the image has a limited region of support (ROS), it is possible to reconstruct the image from a subset of k-space samples. Therefore, we desire to choose the best possible combination of a small number of k-space(More)
Digital forensic investigators are often faced with the task of manually examining a large number of (photographic) images in order to identify potential evidence. The task can be especially daunting and time-consuming if the target of the investigation is very broad, such as a web hosting service. Current forensic tools are woefully inadequate in(More)
Traditional digital forensics methods are based on the in-depth examination of computer systems in a lab setting. Such methods are standard practice in acquiring digital evidence and are indispensable as an investigative approach. However, they are also relatively heavyweight and expensive and require significant expertise on part of the investigator. Thus,(More)
— We use statistical estimates of the entropy rate of spike train data in order to make inferences about the underlying structure of the spike train itself. We first examine a number of different parametric and nonparametric estimators (some known and some new), including the " plug-in " method, several versions of Lempel-Ziv-based compression algorithms, a(More)