Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a ‘neural cursor’ with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis. The cover shows Matt Nagle, first participant in the BrainGate pilot clinical trial. He is unable to move his arms or legs following cervical spinal cord injury. Researchers at the Department of Neuroscience at Brown University, working with biotech company Cyberkinetics and 3 other institutions, demonstrate that movement-related signals can be relayed from the brain via an implanted BrainGate chip, allowing the patient to drive a computer screen cursor and activate simple robotic devices. Such neuromotor prostheses could pave the way towards systems to replace or restore lost motor function in paralysed humans. Prior to this advance, this type of work has been performed chiefly in monkeys. The latest example of such research has achieved a large increase in speed over current devices, enhancing the prospects for clinically viable brain-machine interfaces.

Neuronal ensemble control of prosthetic devices by a human with tetraplegia

Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

/pdf/functional-and-effective-connectivity-a-review-s5oik3vige.pdf

Functional and effective connectivity: a review.

The EEG-controlled MoreGrasp grasp neuroprosthesis for individuals with high spinal cord injury – multipad electrodes for screening and closed-loop grasp pattern control

TOBI interface A (TiA) describes a standardized interface to transmit raw biosignals, supporting multirate and block-oriented transmission of different kinds of signals at the same time. To facilitate a distinction between those kinds of signals, so-called signal types are introduced. TiA is a single server, multiple client system, whereby clients can connect to the server at runtime. Meta information transfer between client and server is divided into a control- and data connection. The control communication is using TCP with XML messages, and data transmission is using UDP or TCP with a binary data stream.

TOBI Interface A (TiA) – A Standardized Interface to Transmit Raw Biosignals

Brain-Computer Interfaces (BCIs) can be extended by other input signals to form a so-called hybrid BCI (hBCI). Such an hBCI allows the processing of several input signals with at least one brain signal for control purposes, i.e. communication and environmental control. This work shows the principle, technology and application of hBCIs and discusses future objectives.

/pdf/the-hybrid-brain-computer-interface-a-bridge-to-assistive-527eg53366.pdf

The hybrid Brain-Computer Interface: a bridge to assistive technology?

Combining Hybrid BCI and signal quality monitoring to improve user experience

Introduction: Brain-Computer Interfaces (BCIs) enable its users to interact with their environment only by thought. Earlier studies indicated [1, 2] that BCI might be a suitable method for controlling a neuroprostheses, which could assist people with spinal cord injuries (SCI) in their daily life. One drawback for the end user is that only simple motor imaginations (MI) are available for control e.g. MI of both feet to control ones arm is abstract and in contradiction to an associated natural movement. Therefore we are looking for means to design a more natural control modality. One promising scenario would be to use MI of different grasps to actually control different grasps of the neuroprosthesis. In this study we attempt to classify the execution of different grasp types in low-frequency time-domain EEG signals. Methods: Fifteen healthy participants from age 23 to 37 participated in the experiment. In a cue guided paradigm (see figure 1), subjects were instructed to perform 3 different reach-grasp-hold tasks on 3 different objects: palmar grasp (cylinder), pincer grasp (needle) and key grasp (key). To introduce a control condition, one spot was deliberately left empty and users were asked to not perform any movement. We recorded 72 trials per condition (288 in total) over 8 runs and varied the position of the objects so that every object was positioned equally often on each position. We recorded 61 active electrodes (g.tec, g.GAMMAsys) as well as data from a data glove (5DT) and a switch button to obtain the movement onset. We rejected artifact contaminated trials and channels using a statistical outlier rejection. We down-sampled the EEG to 16 Hz and applied a bandpass-filter between 0.3 and 3 Hz (4th order, Butterworth, zero-phase) to extract the low-frequency signal. Using 5 fold crossvalidation to avoid overfitting and a random forests classifier [3], we investigated all grasp versus grasp combinations. To score significantly higher than chance level (p = 0.05, Bonferroni corrected for multiple comparisons over trial time), the accuracy level had to be higher than 64.7 %. Table 1 displays the results.

Gernot Müller-Putz

Papers

The EEG-controlled MoreGrasp grasp neuroprosthesis for individuals with high spinal cord injury – multipad electrodes for screening and closed-loop grasp pattern control

TOBI Interface A (TiA) – A Standardized Interface to Transmit Raw Biosignals

The hybrid Brain-Computer Interface: a bridge to assistive technology?

Combining Hybrid BCI and signal quality monitoring to improve user experience

The grasp detected: Time domain classification of grasp and hold tasks