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

The power of feedback.

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

/pdf/a-review-of-classification-algorithms-for-eeg-based-brain-p1h22hb1wr.pdf

A review of classification algorithms for EEG-based brain–computer interfaces

Objective While promising, ElectroEncephaloGraphy based Brain-Computer Interfaces (BCIs) remain barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users' Motor-Imagery based BCI (MI-BCI) control performance. Approach We performed two experiments. The first consisted in evaluating a standard BCI training protocol efficiency for the acquisition of non-BCI related skills in a BCI-free context, which enabled to rule out the possible impact of BCIs on the training outcome. Thus, participants (N=54) were asked to perform simple motor-tasks. The second experiment aimed at measuring the correlations between motor-task and MI-BCI performances. The 10 best and 10 worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform 2 MI-tasks. We also assessed users' spatial abilities and pre-training mu rhythm amplitude, as both have been related to MI-BCI performance in the literature. Main Results Around 17% of the participants were unable to learn to perform the motor-tasks, which is close to the BCI-illiteracy rate. It suggests that standard training protocols are suboptimal for skill-teaching. No correlation was found between motor-task and MI-BCI performance. However, spatial abilities played an important role in MI-BCI performance. Besides, once the " spatial ability" covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. Significance These studies suggest that 1) standard MI-BCI training protocols are suboptimal for skill-teaching, 2) spatial abilities are confirmed to impact MI-BCI performances and 3) when faced with difficult pre-training, subjects seem to explore more strategies and therefore learn better.

https://hal.inria.fr/hal-01302154/document

Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study

Brain–Computer Interfaces (BCI) are communication systems which can convey messages through brain activity alone. Recently BCIs were gaining interest among the virtual reality (VR) community since they have appeared as promising interaction devices for virtual environments (VEs). Especially these implicit interaction techniques are of great interest for the VR community, e.g., you are imaging the movement of your hand and the virtual hand is moving, or you can navigate through houses or museums by your thoughts alone or just by looking at some highlighted objects. Furthermore, VE can provide an excellent testing ground for procedures that could be adapted to real world scenarios, especially patients with disabilities can learn to control their movements or perform specific tasks in a VE. Several studies will highlight these interactions.

/pdf/combining-bci-with-virtual-reality-towards-new-applications-1pjq5h5xuq.pdf

Combining BCI with Virtual Reality: Towards New Applications and Improved BCI

While being very promising for a wide range of applications, Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. Indeed, 10 to 30% of users are unable to control MI-BCIs (so-called " BCI illiteracy ") while only a small proportion reach acceptable control abilities. This huge inter-user variability has led the community to investigate potential predictors of performance related to users' personality and cognitive profile. Based on a literature review, we propose a classification of these MI-BCI performance predictors into three categories representing high-level cognitive concepts: (1) users' relationship with the technology (including the notions of computer-anxiety and sense of agency), (2) attention and (3) spatial abilities. We detail these concepts and their neural correlates in order to better understand their relationship with MI-BCI user-training. Consequently, we propose, by way of future prospects, some guidelines to improve MI-BCI user-training.

Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates.

We couple standardized low-resolution electromagnetic tomography, an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (brain-computer interface) competition 2003, data set IV. The data set is from an experiment where a subject performed a self-paced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabelled test trials have to be classified. The EEG data from 28 electrodes comprise the recording of the 500 ms before the actual finger movements, hence represent uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and our extraction of only one attribute per class, our method yields accuracy similar to the winners of the competition for this data set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on an EEG inverse solution, which rely either on iterative algorithms for single-trial independent component analysis or on trained classifiers.

/pdf/classification-of-movement-intention-by-spatially-filtered-47p16v1p53.pdf

Classification of movement intention by spatially filtered electromagnetic inverse solutions

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field.

/pdf/electroencephalography-eeg-based-brain-computer-interfaces-eo7c806skd.pdf

Fabien Lotte

Papers

Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study

Combining BCI with Virtual Reality: Towards New Applications and Improved BCI

Advances in user-training for mental-imagery-based BCI control: Psychological and cognitive factors and their neural correlates.

Classification of movement intention by spatially filtered electromagnetic inverse solutions

Electroencephalography (EEG)-Based Brain–Computer Interfaces