Joline M. Fan

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Neural prostheses translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, and thus offer individuals with disabilities greater interaction with the world. However, relatively low performance remains a critical barrier to successful clinical translation; current neural(More)
Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation(More)
OBJECTIVE The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF). APPROACH This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural(More)
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during(More)
PURPOSE Treatment response in cancer has been monitored by measuring anatomic tumor volume (ATV) at various times without considering the inherent functional tumor heterogeneity known to critically influence ultimate treatment outcome: primary tumor control and survival. This study applied dynamic contrast-enhanced (DCE) functional MRI to characterize(More)
Brain-machine interfaces (BMIs) aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic arms, computer cursors, and other assistive devices. Animal models are central to the development of these systems and have helped enable the successful translation of the first generation of BMIs. As we move(More)
Communication neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuously moving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many(More)
OBJECTIVE Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of(More)
We present a novel brain machine interface (BMI) control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF). The design of ReFIT-KF is motivated from a feedback control perspective applied to existing BMI control algorithms. The result is two design innovations that alter the modeling assumptions made by these algorithms and the(More)
Behavior often depends on the ability to update beliefs according to new information. Optimization of this process often requires preferential use of information occurring after likely environmental changes. Here we examined the role of the anterior cingulate cortex (ACC) in this process by measuring behavior in two rhesus monkeys and single-unit activity(More)