From thought to action

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references.Systems engineering is rapidly assuming a prominent role in neuroscience that could unify scientific theories, experimental evidence, and medical development. In this three-part work, I study the neural representation of targets before reaching movements and the generation of prosthetic control signals through stochastic modeling and estimation. In the first part, I show that temporal and history dependence contributes to the representation of targets in the ensemble spiking activity of neurons in primate dorsal premotor cortex (PMd). Point process modeling of target representation suggests that local and possibly also distant neural interactions influence the spiking patterns observed in PMd. In the second part, I draw on results from surveillance theory to reconstruct reaching movements from neural activity related to the desired target and the path to that target. This approach combines movement planning and execution to surpass estimation with either target or path related neural activity alone. In the third part, I describe the principled design of brain-driven neural prosthetic devices as a filtering problem on interacting discrete and continuous random processes. This framework subsumes four canonical Bayesian approaches and supports emerging applications to neural prosthetic devices.(cont.) Results of a simulated reaching task predict that the method outperforms previous approaches in the control of arm position and velocity based on trajectory and endpoint mean squared error. These results form the starting point for a systems engineering approach to the design and interpretation of neuroscience experiments that can guide the development of technology for human-computer interaction and medical treatment.by Lakshminarayan Srinivasan.Ph.D

Rapid Decoding of Hand Gestures in Electrocorticography Using Recurrent Neural Networks

Brain-computer interface (BCI) is a direct communication pathway between brain and external devices, and BCI-based prosthetic devices are promising to provide new rehabilitation options for people with motor disabilities. Electrocorticography (ECoG) signals contain rich information correlated with motor activities, and have great potential in hand gesture decoding. However, most existing decoders use long time windows, thus ignore the temporal dynamics within the period. In this study, we propose to use recurrent neural networks (RNNs) to exploit the temporal information in ECoG signals for robust hand gesture decoding. With RNN's high nonlinearity modeling ability, our method can effectively capture the temporal information in ECoG time series for robust gesture recognition. In the experiments, we decode three hand gestures using ECoG signals of two participants, and achieve an accuracy of 90%. Specially, we investigate the possibility of recognizing the gestures in a time interval as short as possible after motion onsets. Our method rapidly recognizes gestures within 0.5 s after motion onsets with an accuracy of about 80%. Experimental results also indicate that the temporal dynamics is especially informative for effective and rapid decoding of hand gestures

Modern Developments in Transcranial Magnetic Stimulation (TMS) – Applications and Perspectives in Clinical Neuroscience

Transcranial magnetic stimulation (TMS) is being increasingly used in neuroscience and clinics. Modern advances include but are not limited to the combination of TMS with precise neuronavigation as well as the integration of TMS into a multimodal environment, e.g., by guiding the TMS application using complementary techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), diffusion tensor imaging (DTI), or magnetoencephalography (MEG). Furthermore, the impact of stimulation can be identified and characterized by such multimodal approaches, helping to shed light on the basic neurophysiology and TMS effects in the human brain. Against this background, the aim of this Special Issue was to explore advancements in the field of TMS considering both investigations in healthy subjects as well as patients