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

There are numerous possibilities and motivations for an adaptive BCI, which may not be easy to clarify and organize for a newcomer to the field. To our knowledge, there has not been any work done in classifying the literature on adaptive BCI in a comprehensive and structured way. We propose a conceptual framework, a taxonomy of adaptive BCI methods which encompasses most important approaches to fit them in such a way that a reader can clearly visualize which elements are being adapted and for what reason. In the interest of having a clear review of existing adaptive BCIs, this framework considers adaptation approaches for both the user and the machine, i.e., using instructional design observations as well as the usual machine learning techniques. This framework not only provides a coherent review of such extensive literature but also enables the reader to perceive gaps and flaws in the current BCI systems, which would hopefully bring novel solutions for an overall improvement.

A Generic Framework for Adaptive EEG-Based BCI Training and Operation

One of the major limitations of Brain-Computer Interfaces (BCI) is their long calibration time. This is due to the need to collect numerous training EEG trials for the machine learning algorithm used in their design. In this paper we propose a new approach to reduce this calibration time. This approach consists in generating arti ficial EEG trials from the few EEG trials initially available, in order to augment the training set size in a relevant way. The approach followed is simple and computationally efficient. Moreover, our offline evaluations suggested that it can lead to signi ficant increases in classification accuracy when compared with existing approaches, especially when the number of training trials available is small. As such, it can indeed be used to reduce calibration time.

https://hal.inria.fr/inria-00599325/document

Generating Artificial EEG Signals To Reduce BCI Calibration Time

We read with great interest the recent article of Schabus et al. entitled ‘Better than sham? A double-blind placebo-controlled neurofeedback study in primary insomnia’ published in Brain (Schabus et al., 2017) and its commentary ‘Neurofeedback or neuroplacebo?’ (Thibault et al., 2017)...

https://hal.inria.fr/hal-01658935/document

On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms?

This second volume, Technology and Applications, is focused on the field of BCI from the perspective of its end users, such as those with disabilities to practitioners. Covering clinical applications and the field of video games, the book then goes on to explore user needs which drive the design and development of BCI. The software used for their design, primarily OpenViBE, is explained step by step, before a discussion on the use of BCI from ethical, philosophical and social perspectives.

Brain-Computer Interfaces 2: Technology and Applications

https://depositonce.tu-berlin.de/bitstream/11303/12350/2/Krol_etal_SEREEGA_2018.pdf

Fabien Lotte

Papers

A Generic Framework for Adaptive EEG-Based BCI Training and Operation

Generating Artificial EEG Signals To Reduce BCI Calibration Time

On assessing neurofeedback effects: should double-blind replace neurophysiological mechanisms?

Brain-Computer Interfaces 2: Technology and Applications

SEREEGA: Simulating event-related EEG activity