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.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3–45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25–63% improvement), and VEPs variability (16–44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

/pdf/online-reduction-of-artifacts-in-eeg-of-simultaneous-eeg-ti9hcks440.pdf

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF)

Graz-Brain-Computer Interface: State of Research

Mutual brain-machine co-adaptation is the most common approach used to gain control over spontaneous electroencephalogram (EEG) based brain-computer interfaces (BCIs). Co-adaptation means the concurrent or alternating use of machine learning and the brain's reinforcement learning mechanisms. Results from the literature, however, suggest that current implementations of this approach does not lead to desired results ("BCI inefficiency"). In this paper, we propose an alternative strategy that implements some recommendations from educational psychology and instructional design. We present a jigsaw puzzle game for Android devices developed to train the BCI skill in individuals with cerebral palsy (CP). Preliminary results of a supporting study in four CP users suggest high user acceptance. Three out of the four users achieved better than chance accuracy in arranging pieces to form the puzzle.

Game-Based BCI Training: Interactive Design for Individuals with Cerebral Palsy

A Brain-Computer Interface (BCI) represents the ultimate means of communication for people with severe paralyses or who are in a locked-in state. However, the usage of BCI is still severely limited in terms of accuracy and performance speed. One possible way to overcome these restrictions would be the detection of errors after incorrect events in the electroencephalogram (EEG). In this study 13 subjects participated in a first experiment to provide data for offline analysis of interaction error potentials (ErrPs) which were recorded after observation of falsely interpreted user-commands by an interface. These characteristic waveforms were later used to classify errors in online experiments combined with motor imagery (MI). Here, the detection of false movements could improve the accuracy significantly.

Implementation of Error Detection into the Graz-Brain-Computer Interface, the Interaction Error Potential

The main objective of this roadmap is to provide a global perspective on the BCI field now and in the future. For readers not familiar with BCIs, we introduce basic terminology and concepts. We discuss what BCIs are, what BCIs can do, and who can benefit from BCIs. We illustrate our arguments with use cases to support the main messages. After reading this roadmap you will have a clear picture of the potential benefits and challenges of BCIs, the steps necessary to bridge the gap between current and future applications, and the potential impact of BCIs on society in the next decade and beyond.

Gernot Müller-Putz

Papers

Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF)

Graz-Brain-Computer Interface: State of Research

Game-Based BCI Training: Interactive Design for Individuals with Cerebral Palsy

Implementation of Error Detection into the Graz-Brain-Computer Interface, the Interaction Error Potential

The future in brain/neural computer interaction: Horizon 2020